5.Results_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<summary>Table of Contents</summary>\n",
    "\n",
    "- [1. Input length](#1-input-length)\n",
    "- [2. Comparison of models](#2-comparison-of-models)\n",
    "- [3. Self-supervised](#3-self-supervised)\n",
    "- [4. Ablation study](#4-ablation-study)\n",
    "    - [4.1. W/o RevIN](#41-wo-revin)\n",
    "    - [4.2 W/o channel independence](#42-wo-channel-independence-channel-mixing)\n",
    "    - [4.3 W/o patching](#43-wo-patching)\n",
    "- [5. TimeLLM](#5-timellm)\n",
    "- [6. Decomposition](#6-decomposition)\n",
    "\n",
    "This script summarizes results from all experiments and represents them in plots and tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from utils.helper import style_dataframe, \\\n",
    "                         map_country_name, \\\n",
    "                         read_results_csv, \\\n",
    "                         choose_best_model, \\\n",
    "                         calculate_improvement\n",
    "                         \n",
    "from utils.plot_final_results import plot_results_models_multiple_countries\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Input length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table id=\"T_6ade3\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"index_name level0\" >Model</th>\n",
       "      <th id=\"T_6ade3_level0_col0\" class=\"col_heading level0 col0\" colspan=\"2\">PatchTST/21</th>\n",
       "      <th id=\"T_6ade3_level0_col2\" class=\"col_heading level0 col2\" colspan=\"2\">PatchTST/42</th>\n",
       "      <th id=\"T_6ade3_level0_col4\" class=\"col_heading level0 col4\" colspan=\"2\">PatchTST/64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"index_name level1\" >Metrics</th>\n",
       "      <th id=\"T_6ade3_level1_col0\" class=\"col_heading level1 col0\" >RMSE</th>\n",
       "      <th id=\"T_6ade3_level1_col1\" class=\"col_heading level1 col1\" >MAE</th>\n",
       "      <th id=\"T_6ade3_level1_col2\" class=\"col_heading level1 col2\" >RMSE</th>\n",
       "      <th id=\"T_6ade3_level1_col3\" class=\"col_heading level1 col3\" >MAE</th>\n",
       "      <th id=\"T_6ade3_level1_col4\" class=\"col_heading level1 col4\" >RMSE</th>\n",
       "      <th id=\"T_6ade3_level1_col5\" class=\"col_heading level1 col5\" >MAE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Country</th>\n",
       "      <th class=\"index_name level1\" >Pred_len</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6ade3_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"3\">Germany</th>\n",
       "      <th id=\"T_6ade3_level1_row0\" class=\"row_heading level1 row0\" >24</th>\n",
       "      <td id=\"T_6ade3_row0_col0\" class=\"data row0 col0\" >14.47</td>\n",
       "      <td id=\"T_6ade3_row0_col1\" class=\"data row0 col1\" >9.14</td>\n",
       "      <td id=\"T_6ade3_row0_col2\" class=\"data row0 col2\" >14.43</td>\n",
       "      <td id=\"T_6ade3_row0_col3\" class=\"data row0 col3\" >9.11</td>\n",
       "      <td id=\"T_6ade3_row0_col4\" class=\"data row0 col4\" >14.47</td>\n",
       "      <td id=\"T_6ade3_row0_col5\" class=\"data row0 col5\" >9.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6ade3_level1_row1\" class=\"row_heading level1 row1\" >96</th>\n",
       "      <td id=\"T_6ade3_row1_col0\" class=\"data row1 col0\" >19.12</td>\n",
       "      <td id=\"T_6ade3_row1_col1\" class=\"data row1 col1\" >12.98</td>\n",
       "      <td id=\"T_6ade3_row1_col2\" class=\"data row1 col2\" >18.89</td>\n",
       "      <td id=\"T_6ade3_row1_col3\" class=\"data row1 col3\" >12.91</td>\n",
       "      <td id=\"T_6ade3_row1_col4\" class=\"data row1 col4\" >18.61</td>\n",
       "      <td id=\"T_6ade3_row1_col5\" class=\"data row1 col5\" >12.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6ade3_level1_row2\" class=\"row_heading level1 row2\" >168</th>\n",
       "      <td id=\"T_6ade3_row2_col0\" class=\"data row2 col0\" >20.15</td>\n",
       "      <td id=\"T_6ade3_row2_col1\" class=\"data row2 col1\" >13.82</td>\n",
       "      <td id=\"T_6ade3_row2_col2\" class=\"data row2 col2\" >19.58</td>\n",
       "      <td id=\"T_6ade3_row2_col3\" class=\"data row2 col3\" >13.66</td>\n",
       "      <td id=\"T_6ade3_row2_col4\" class=\"data row2 col4\" >19.27</td>\n",
       "      <td id=\"T_6ade3_row2_col5\" class=\"data row2 col5\" >13.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6ade3_level0_row3\" class=\"row_heading level0 row3\" rowspan=\"3\">United Kingdom</th>\n",
       "      <th id=\"T_6ade3_level1_row3\" class=\"row_heading level1 row3\" >24</th>\n",
       "      <td id=\"T_6ade3_row3_col0\" class=\"data row3 col0\" >15.88</td>\n",
       "      <td id=\"T_6ade3_row3_col1\" class=\"data row3 col1\" >10.29</td>\n",
       "      <td id=\"T_6ade3_row3_col2\" class=\"data row3 col2\" >15.84</td>\n",
       "      <td id=\"T_6ade3_row3_col3\" class=\"data row3 col3\" >10.36</td>\n",
       "      <td id=\"T_6ade3_row3_col4\" class=\"data row3 col4\" >15.65</td>\n",
       "      <td id=\"T_6ade3_row3_col5\" class=\"data row3 col5\" >10.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6ade3_level1_row4\" class=\"row_heading level1 row4\" >96</th>\n",
       "      <td id=\"T_6ade3_row4_col0\" class=\"data row4 col0\" >21.12</td>\n",
       "      <td id=\"T_6ade3_row4_col1\" class=\"data row4 col1\" >14.62</td>\n",
       "      <td id=\"T_6ade3_row4_col2\" class=\"data row4 col2\" >20.57</td>\n",
       "      <td id=\"T_6ade3_row4_col3\" class=\"data row4 col3\" >14.37</td>\n",
       "      <td id=\"T_6ade3_row4_col4\" class=\"data row4 col4\" >20.02</td>\n",
       "      <td id=\"T_6ade3_row4_col5\" class=\"data row4 col5\" >14.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6ade3_level1_row5\" class=\"row_heading level1 row5\" >168</th>\n",
       "      <td id=\"T_6ade3_row5_col0\" class=\"data row5 col0\" >21.86</td>\n",
       "      <td id=\"T_6ade3_row5_col1\" class=\"data row5 col1\" >15.31</td>\n",
       "      <td id=\"T_6ade3_row5_col2\" class=\"data row5 col2\" >21.32</td>\n",
       "      <td id=\"T_6ade3_row5_col3\" class=\"data row5 col3\" >15.11</td>\n",
       "      <td id=\"T_6ade3_row5_col4\" class=\"data row5 col4\" >20.62</td>\n",
       "      <td id=\"T_6ade3_row5_col5\" class=\"data row5 col5\" >14.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6ade3_level0_row6\" class=\"row_heading level0 row6\" rowspan=\"3\">Spain</th>\n",
       "      <th id=\"T_6ade3_level1_row6\" class=\"row_heading level1 row6\" >24</th>\n",
       "      <td id=\"T_6ade3_row6_col0\" class=\"data row6 col0\" >9.88</td>\n",
       "      <td id=\"T_6ade3_row6_col1\" class=\"data row6 col1\" >6.08</td>\n",
       "      <td id=\"T_6ade3_row6_col2\" class=\"data row6 col2\" >9.87</td>\n",
       "      <td id=\"T_6ade3_row6_col3\" class=\"data row6 col3\" >6.07</td>\n",
       "      <td id=\"T_6ade3_row6_col4\" class=\"data row6 col4\" >9.88</td>\n",
       "      <td id=\"T_6ade3_row6_col5\" class=\"data row6 col5\" >6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6ade3_level1_row7\" class=\"row_heading level1 row7\" >96</th>\n",
       "      <td id=\"T_6ade3_row7_col0\" class=\"data row7 col0\" >13.71</td>\n",
       "      <td id=\"T_6ade3_row7_col1\" class=\"data row7 col1\" >8.83</td>\n",
       "      <td id=\"T_6ade3_row7_col2\" class=\"data row7 col2\" >13.49</td>\n",
       "      <td id=\"T_6ade3_row7_col3\" class=\"data row7 col3\" >8.79</td>\n",
       "      <td id=\"T_6ade3_row7_col4\" class=\"data row7 col4\" >13.68</td>\n",
       "      <td id=\"T_6ade3_row7_col5\" class=\"data row7 col5\" >9.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6ade3_level1_row8\" class=\"row_heading level1 row8\" >168</th>\n",
       "      <td id=\"T_6ade3_row8_col0\" class=\"data row8 col0\" >14.54</td>\n",
       "      <td id=\"T_6ade3_row8_col1\" class=\"data row8 col1\" >9.51</td>\n",
       "      <td id=\"T_6ade3_row8_col2\" class=\"data row8 col2\" >14.21</td>\n",
       "      <td id=\"T_6ade3_row8_col3\" class=\"data row8 col3\" >9.48</td>\n",
       "      <td id=\"T_6ade3_row8_col4\" class=\"data row8 col4\" >14.52</td>\n",
       "      <td id=\"T_6ade3_row8_col5\" class=\"data row8 col5\" >9.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6ade3_level0_row9\" class=\"row_heading level0 row9\" rowspan=\"3\">France</th>\n",
       "      <th id=\"T_6ade3_level1_row9\" class=\"row_heading level1 row9\" >24</th>\n",
       "      <td id=\"T_6ade3_row9_col0\" class=\"data row9 col0\" >9.97</td>\n",
       "      <td id=\"T_6ade3_row9_col1\" class=\"data row9 col1\" >5.66</td>\n",
       "      <td id=\"T_6ade3_row9_col2\" class=\"data row9 col2\" >10.02</td>\n",
       "      <td id=\"T_6ade3_row9_col3\" class=\"data row9 col3\" >5.81</td>\n",
       "      <td id=\"T_6ade3_row9_col4\" class=\"data row9 col4\" >10.04</td>\n",
       "      <td id=\"T_6ade3_row9_col5\" class=\"data row9 col5\" >5.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6ade3_level1_row10\" class=\"row_heading level1 row10\" >96</th>\n",
       "      <td id=\"T_6ade3_row10_col0\" class=\"data row10 col0\" >13.63</td>\n",
       "      <td id=\"T_6ade3_row10_col1\" class=\"data row10 col1\" >8.20</td>\n",
       "      <td id=\"T_6ade3_row10_col2\" class=\"data row10 col2\" >13.66</td>\n",
       "      <td id=\"T_6ade3_row10_col3\" class=\"data row10 col3\" >8.42</td>\n",
       "      <td id=\"T_6ade3_row10_col4\" class=\"data row10 col4\" >13.74</td>\n",
       "      <td id=\"T_6ade3_row10_col5\" class=\"data row10 col5\" >8.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6ade3_level1_row11\" class=\"row_heading level1 row11\" >168</th>\n",
       "      <td id=\"T_6ade3_row11_col0\" class=\"data row11 col0\" >14.26</td>\n",
       "      <td id=\"T_6ade3_row11_col1\" class=\"data row11 col1\" >8.86</td>\n",
       "      <td id=\"T_6ade3_row11_col2\" class=\"data row11 col2\" >14.34</td>\n",
       "      <td id=\"T_6ade3_row11_col3\" class=\"data row11 col3\" >9.05</td>\n",
       "      <td id=\"T_6ade3_row11_col4\" class=\"data row11 col4\" >14.83</td>\n",
       "      <td id=\"T_6ade3_row11_col5\" class=\"data row11 col5\" >9.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6ade3_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"3\">Italy</th>\n",
       "      <th id=\"T_6ade3_level1_row12\" class=\"row_heading level1 row12\" >24</th>\n",
       "      <td id=\"T_6ade3_row12_col0\" class=\"data row12 col0\" >9.95</td>\n",
       "      <td id=\"T_6ade3_row12_col1\" class=\"data row12 col1\" >5.82</td>\n",
       "      <td id=\"T_6ade3_row12_col2\" class=\"data row12 col2\" >9.98</td>\n",
       "      <td id=\"T_6ade3_row12_col3\" class=\"data row12 col3\" >5.81</td>\n",
       "      <td id=\"T_6ade3_row12_col4\" class=\"data row12 col4\" >10.06</td>\n",
       "      <td id=\"T_6ade3_row12_col5\" class=\"data row12 col5\" >5.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6ade3_level1_row13\" class=\"row_heading level1 row13\" >96</th>\n",
       "      <td id=\"T_6ade3_row13_col0\" class=\"data row13 col0\" >13.15</td>\n",
       "      <td id=\"T_6ade3_row13_col1\" class=\"data row13 col1\" >7.98</td>\n",
       "      <td id=\"T_6ade3_row13_col2\" class=\"data row13 col2\" >13.36</td>\n",
       "      <td id=\"T_6ade3_row13_col3\" class=\"data row13 col3\" >8.16</td>\n",
       "      <td id=\"T_6ade3_row13_col4\" class=\"data row13 col4\" >13.30</td>\n",
       "      <td id=\"T_6ade3_row13_col5\" class=\"data row13 col5\" >8.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6ade3_level1_row14\" class=\"row_heading level1 row14\" >168</th>\n",
       "      <td id=\"T_6ade3_row14_col0\" class=\"data row14 col0\" >13.75</td>\n",
       "      <td id=\"T_6ade3_row14_col1\" class=\"data row14 col1\" >8.58</td>\n",
       "      <td id=\"T_6ade3_row14_col2\" class=\"data row14 col2\" >14.06</td>\n",
       "      <td id=\"T_6ade3_row14_col3\" class=\"data row14 col3\" >8.75</td>\n",
       "      <td id=\"T_6ade3_row14_col4\" class=\"data row14 col4\" >13.73</td>\n",
       "      <td id=\"T_6ade3_row14_col5\" class=\"data row14 col5\" >8.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f24fbe95ed0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patchtst_df = read_results_csv('patchtst/patchtst_128.csv',\n",
    "                               columns_to_extract = ('PatchTST/42', ['RMSE', 'MAE'])\n",
    "                               )\n",
    "patchtst_21_df = read_results_csv('patchtst/patchtst_168_21_patch.csv',\n",
    "                                   columns_to_extract = ('PatchTST/21', ['RMSE', 'MAE'])\n",
    "                                   )\n",
    "patchtst_64_df = read_results_csv('patchtst/patchtst_bs128_pl512.csv',\n",
    "                                   columns_to_extract = ('PatchTST/64', ['RMSE', 'MAE'])\n",
    "                                   )\n",
    "\n",
    "all_df_store = pd.concat([patchtst_21_df, patchtst_df, patchtst_64_df], axis=1)\n",
    "\n",
    "# Change country codes to country names\n",
    "level_names = all_df_store.index.names\n",
    "all_df_store.index = pd.MultiIndex.from_tuples(\n",
    "    [(map_country_name(x[0]), x[1]) for x in all_df_store.index],\n",
    "    names=level_names\n",
    ")\n",
    "# Scale\n",
    "scale = 10**2\n",
    "all_df_scale_store = all_df_store * scale\n",
    "\n",
    "# With best PatchTST configurations\n",
    "result_df_unscaled = choose_best_model(all_df_store,\n",
    "                                       'PatchTST')\n",
    "result_df = result_df_unscaled * scale\n",
    "\n",
    "# Highlight the minimal values in bold\n",
    "styled_df = style_dataframe(all_df_scale_store, decimal_places=2)\n",
    "styled_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average improvement of MAE: 1.71 %.\n",
      "Average improvement of RMSE: 1.42 %.\n"
     ]
    }
   ],
   "source": [
    "best_vs_base = pd.concat([result_df, all_df_scale_store.loc[:, all_df_store.columns.get_level_values('Model') == 'PatchTST/42']], axis=1)\n",
    "best_vs_base = calculate_improvement(best_vs_base, \n",
    "                                      base_mae_model='PatchTST', \n",
    "                                      model_to_compare_mae='PatchTST/42',\n",
    "                                      grouped_by_models=False)\n",
    "print(f\"Average improvement of MAE: {(best_vs_base.loc[best_vs_base['mae_improvement']!=0]['mae_improvement'].mean()).round(2)} %.\")\n",
    "print(f\"Average improvement of RMSE: {(best_vs_base.loc[best_vs_base['rmse_improvement']!=0]['rmse_improvement'].mean()).round(2)} %.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save styled dataframe to HTML\n",
    "path = './stored_elements/tables'\n",
    "\n",
    "if not os.path.exists(path):\n",
    "    os.makedirs(path)\n",
    "styled_df.to_html(os.path.join(path, 'patchtst_input_length_10_power_2.html'), index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Comparison of models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  text-decoration: underline;\n",
       "}\n",
       "#T_52283_row0_col1, #T_52283_row0_col4, #T_52283_row1_col0, #T_52283_row1_col1, #T_52283_row2_col0, #T_52283_row2_col1, #T_52283_row3_col1, #T_52283_row3_col4, #T_52283_row4_col0, #T_52283_row4_col1, #T_52283_row5_col0, #T_52283_row5_col1, #T_52283_row6_col1, #T_52283_row6_col4, #T_52283_row7_col0, #T_52283_row7_col1, #T_52283_row8_col0, #T_52283_row8_col1, #T_52283_row9_col1, #T_52283_row9_col4, #T_52283_row10_col1, #T_52283_row10_col4, #T_52283_row11_col0, #T_52283_row11_col1, #T_52283_row12_col1, #T_52283_row12_col4, #T_52283_row13_col1, #T_52283_row13_col4, #T_52283_row14_col0, #T_52283_row14_col1 {\n",
       "  font-weight: bold;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_52283\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"index_name level0\" >Model</th>\n",
       "      <th id=\"T_52283_level0_col0\" class=\"col_heading level0 col0\" colspan=\"2\">PatchTST</th>\n",
       "      <th id=\"T_52283_level0_col2\" class=\"col_heading level0 col2\" colspan=\"2\">Informer</th>\n",
       "      <th id=\"T_52283_level0_col4\" class=\"col_heading level0 col4\" colspan=\"2\">(S)ARIMA</th>\n",
       "      <th id=\"T_52283_level0_col6\" class=\"col_heading level0 col6\" colspan=\"2\">Persistence</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"index_name level1\" >Metrics</th>\n",
       "      <th id=\"T_52283_level1_col0\" class=\"col_heading level1 col0\" >RMSE</th>\n",
       "      <th id=\"T_52283_level1_col1\" class=\"col_heading level1 col1\" >MAE</th>\n",
       "      <th id=\"T_52283_level1_col2\" class=\"col_heading level1 col2\" >RMSE</th>\n",
       "      <th id=\"T_52283_level1_col3\" class=\"col_heading level1 col3\" >MAE</th>\n",
       "      <th id=\"T_52283_level1_col4\" class=\"col_heading level1 col4\" >RMSE</th>\n",
       "      <th id=\"T_52283_level1_col5\" class=\"col_heading level1 col5\" >MAE</th>\n",
       "      <th id=\"T_52283_level1_col6\" class=\"col_heading level1 col6\" >RMSE</th>\n",
       "      <th id=\"T_52283_level1_col7\" class=\"col_heading level1 col7\" >MAE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Country</th>\n",
       "      <th class=\"index_name level1\" >Pred_len</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_52283_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"3\">Germany</th>\n",
       "      <th id=\"T_52283_level1_row0\" class=\"row_heading level1 row0\" >24</th>\n",
       "      <td id=\"T_52283_row0_col0\" class=\"data row0 col0\" >14.43</td>\n",
       "      <td id=\"T_52283_row0_col1\" class=\"data row0 col1\" >9.11</td>\n",
       "      <td id=\"T_52283_row0_col2\" class=\"data row0 col2\" >15.97</td>\n",
       "      <td id=\"T_52283_row0_col3\" class=\"data row0 col3\" >10.63</td>\n",
       "      <td id=\"T_52283_row0_col4\" class=\"data row0 col4\" >11.15</td>\n",
       "      <td id=\"T_52283_row0_col5\" class=\"data row0 col5\" >9.32</td>\n",
       "      <td id=\"T_52283_row0_col6\" class=\"data row0 col6\" >19.61</td>\n",
       "      <td id=\"T_52283_row0_col7\" class=\"data row0 col7\" >13.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52283_level1_row1\" class=\"row_heading level1 row1\" >96</th>\n",
       "      <td id=\"T_52283_row1_col0\" class=\"data row1 col0\" >18.61</td>\n",
       "      <td id=\"T_52283_row1_col1\" class=\"data row1 col1\" >12.82</td>\n",
       "      <td id=\"T_52283_row1_col2\" class=\"data row1 col2\" >20.39</td>\n",
       "      <td id=\"T_52283_row1_col3\" class=\"data row1 col3\" >14.68</td>\n",
       "      <td id=\"T_52283_row1_col4\" class=\"data row1 col4\" >20.28</td>\n",
       "      <td id=\"T_52283_row1_col5\" class=\"data row1 col5\" >16.49</td>\n",
       "      <td id=\"T_52283_row1_col6\" class=\"data row1 col6\" >26.14</td>\n",
       "      <td id=\"T_52283_row1_col7\" class=\"data row1 col7\" >19.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52283_level1_row2\" class=\"row_heading level1 row2\" >168</th>\n",
       "      <td id=\"T_52283_row2_col0\" class=\"data row2 col0\" >19.27</td>\n",
       "      <td id=\"T_52283_row2_col1\" class=\"data row2 col1\" >13.54</td>\n",
       "      <td id=\"T_52283_row2_col2\" class=\"data row2 col2\" >21.42</td>\n",
       "      <td id=\"T_52283_row2_col3\" class=\"data row2 col3\" >15.39</td>\n",
       "      <td id=\"T_52283_row2_col4\" class=\"data row2 col4\" >22.79</td>\n",
       "      <td id=\"T_52283_row2_col5\" class=\"data row2 col5\" >18.51</td>\n",
       "      <td id=\"T_52283_row2_col6\" class=\"data row2 col6\" >23.39</td>\n",
       "      <td id=\"T_52283_row2_col7\" class=\"data row2 col7\" >17.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52283_level0_row3\" class=\"row_heading level0 row3\" rowspan=\"3\">United Kingdom</th>\n",
       "      <th id=\"T_52283_level1_row3\" class=\"row_heading level1 row3\" >24</th>\n",
       "      <td id=\"T_52283_row3_col0\" class=\"data row3 col0\" >15.65</td>\n",
       "      <td id=\"T_52283_row3_col1\" class=\"data row3 col1\" >10.27</td>\n",
       "      <td id=\"T_52283_row3_col2\" class=\"data row3 col2\" >17.64</td>\n",
       "      <td id=\"T_52283_row3_col3\" class=\"data row3 col3\" >12.03</td>\n",
       "      <td id=\"T_52283_row3_col4\" class=\"data row3 col4\" >14.41</td>\n",
       "      <td id=\"T_52283_row3_col5\" class=\"data row3 col5\" >12.01</td>\n",
       "      <td id=\"T_52283_row3_col6\" class=\"data row3 col6\" >19.89</td>\n",
       "      <td id=\"T_52283_row3_col7\" class=\"data row3 col7\" >14.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52283_level1_row4\" class=\"row_heading level1 row4\" >96</th>\n",
       "      <td id=\"T_52283_row4_col0\" class=\"data row4 col0\" >20.02</td>\n",
       "      <td id=\"T_52283_row4_col1\" class=\"data row4 col1\" >14.09</td>\n",
       "      <td id=\"T_52283_row4_col2\" class=\"data row4 col2\" >22.50</td>\n",
       "      <td id=\"T_52283_row4_col3\" class=\"data row4 col3\" >16.01</td>\n",
       "      <td id=\"T_52283_row4_col4\" class=\"data row4 col4\" >21.53</td>\n",
       "      <td id=\"T_52283_row4_col5\" class=\"data row4 col5\" >17.69</td>\n",
       "      <td id=\"T_52283_row4_col6\" class=\"data row4 col6\" >25.54</td>\n",
       "      <td id=\"T_52283_row4_col7\" class=\"data row4 col7\" >19.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52283_level1_row5\" class=\"row_heading level1 row5\" >168</th>\n",
       "      <td id=\"T_52283_row5_col0\" class=\"data row5 col0\" >20.62</td>\n",
       "      <td id=\"T_52283_row5_col1\" class=\"data row5 col1\" >14.66</td>\n",
       "      <td id=\"T_52283_row5_col2\" class=\"data row5 col2\" >25.91</td>\n",
       "      <td id=\"T_52283_row5_col3\" class=\"data row5 col3\" >18.29</td>\n",
       "      <td id=\"T_52283_row5_col4\" class=\"data row5 col4\" >22.67</td>\n",
       "      <td id=\"T_52283_row5_col5\" class=\"data row5 col5\" >18.28</td>\n",
       "      <td id=\"T_52283_row5_col6\" class=\"data row5 col6\" >24.69</td>\n",
       "      <td id=\"T_52283_row5_col7\" class=\"data row5 col7\" >18.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52283_level0_row6\" class=\"row_heading level0 row6\" rowspan=\"3\">Spain</th>\n",
       "      <th id=\"T_52283_level1_row6\" class=\"row_heading level1 row6\" >24</th>\n",
       "      <td id=\"T_52283_row6_col0\" class=\"data row6 col0\" >9.87</td>\n",
       "      <td id=\"T_52283_row6_col1\" class=\"data row6 col1\" >6.07</td>\n",
       "      <td id=\"T_52283_row6_col2\" class=\"data row6 col2\" >17.42</td>\n",
       "      <td id=\"T_52283_row6_col3\" class=\"data row6 col3\" >10.45</td>\n",
       "      <td id=\"T_52283_row6_col4\" class=\"data row6 col4\" >9.68</td>\n",
       "      <td id=\"T_52283_row6_col5\" class=\"data row6 col5\" >7.89</td>\n",
       "      <td id=\"T_52283_row6_col6\" class=\"data row6 col6\" >14.06</td>\n",
       "      <td id=\"T_52283_row6_col7\" class=\"data row6 col7\" >9.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52283_level1_row7\" class=\"row_heading level1 row7\" >96</th>\n",
       "      <td id=\"T_52283_row7_col0\" class=\"data row7 col0\" >13.49</td>\n",
       "      <td id=\"T_52283_row7_col1\" class=\"data row7 col1\" >8.79</td>\n",
       "      <td id=\"T_52283_row7_col2\" class=\"data row7 col2\" >21.24</td>\n",
       "      <td id=\"T_52283_row7_col3\" class=\"data row7 col3\" >13.74</td>\n",
       "      <td id=\"T_52283_row7_col4\" class=\"data row7 col4\" >14.17</td>\n",
       "      <td id=\"T_52283_row7_col5\" class=\"data row7 col5\" >11.22</td>\n",
       "      <td id=\"T_52283_row7_col6\" class=\"data row7 col6\" >19.67</td>\n",
       "      <td id=\"T_52283_row7_col7\" class=\"data row7 col7\" >13.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52283_level1_row8\" class=\"row_heading level1 row8\" >168</th>\n",
       "      <td id=\"T_52283_row8_col0\" class=\"data row8 col0\" >14.21</td>\n",
       "      <td id=\"T_52283_row8_col1\" class=\"data row8 col1\" >9.48</td>\n",
       "      <td id=\"T_52283_row8_col2\" class=\"data row8 col2\" >21.38</td>\n",
       "      <td id=\"T_52283_row8_col3\" class=\"data row8 col3\" >14.08</td>\n",
       "      <td id=\"T_52283_row8_col4\" class=\"data row8 col4\" >17.61</td>\n",
       "      <td id=\"T_52283_row8_col5\" class=\"data row8 col5\" >13.59</td>\n",
       "      <td id=\"T_52283_row8_col6\" class=\"data row8 col6\" >17.74</td>\n",
       "      <td id=\"T_52283_row8_col7\" class=\"data row8 col7\" >12.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52283_level0_row9\" class=\"row_heading level0 row9\" rowspan=\"3\">France</th>\n",
       "      <th id=\"T_52283_level1_row9\" class=\"row_heading level1 row9\" >24</th>\n",
       "      <td id=\"T_52283_row9_col0\" class=\"data row9 col0\" >9.97</td>\n",
       "      <td id=\"T_52283_row9_col1\" class=\"data row9 col1\" >5.66</td>\n",
       "      <td id=\"T_52283_row9_col2\" class=\"data row9 col2\" >11.71</td>\n",
       "      <td id=\"T_52283_row9_col3\" class=\"data row9 col3\" >7.17</td>\n",
       "      <td id=\"T_52283_row9_col4\" class=\"data row9 col4\" >7.60</td>\n",
       "      <td id=\"T_52283_row9_col5\" class=\"data row9 col5\" >6.06</td>\n",
       "      <td id=\"T_52283_row9_col6\" class=\"data row9 col6\" >12.24</td>\n",
       "      <td id=\"T_52283_row9_col7\" class=\"data row9 col7\" >8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52283_level1_row10\" class=\"row_heading level1 row10\" >96</th>\n",
       "      <td id=\"T_52283_row10_col0\" class=\"data row10 col0\" >13.63</td>\n",
       "      <td id=\"T_52283_row10_col1\" class=\"data row10 col1\" >8.20</td>\n",
       "      <td id=\"T_52283_row10_col2\" class=\"data row10 col2\" >14.80</td>\n",
       "      <td id=\"T_52283_row10_col3\" class=\"data row10 col3\" >9.20</td>\n",
       "      <td id=\"T_52283_row10_col4\" class=\"data row10 col4\" >12.54</td>\n",
       "      <td id=\"T_52283_row10_col5\" class=\"data row10 col5\" >9.81</td>\n",
       "      <td id=\"T_52283_row10_col6\" class=\"data row10 col6\" >17.82</td>\n",
       "      <td id=\"T_52283_row10_col7\" class=\"data row10 col7\" >12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52283_level1_row11\" class=\"row_heading level1 row11\" >168</th>\n",
       "      <td id=\"T_52283_row11_col0\" class=\"data row11 col0\" >14.26</td>\n",
       "      <td id=\"T_52283_row11_col1\" class=\"data row11 col1\" >8.86</td>\n",
       "      <td id=\"T_52283_row11_col2\" class=\"data row11 col2\" >15.14</td>\n",
       "      <td id=\"T_52283_row11_col3\" class=\"data row11 col3\" >9.53</td>\n",
       "      <td id=\"T_52283_row11_col4\" class=\"data row11 col4\" >14.89</td>\n",
       "      <td id=\"T_52283_row11_col5\" class=\"data row11 col5\" >11.50</td>\n",
       "      <td id=\"T_52283_row11_col6\" class=\"data row11 col6\" >16.29</td>\n",
       "      <td id=\"T_52283_row11_col7\" class=\"data row11 col7\" >10.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52283_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"3\">Italy</th>\n",
       "      <th id=\"T_52283_level1_row12\" class=\"row_heading level1 row12\" >24</th>\n",
       "      <td id=\"T_52283_row12_col0\" class=\"data row12 col0\" >9.95</td>\n",
       "      <td id=\"T_52283_row12_col1\" class=\"data row12 col1\" >5.82</td>\n",
       "      <td id=\"T_52283_row12_col2\" class=\"data row12 col2\" >10.67</td>\n",
       "      <td id=\"T_52283_row12_col3\" class=\"data row12 col3\" >6.60</td>\n",
       "      <td id=\"T_52283_row12_col4\" class=\"data row12 col4\" >8.64</td>\n",
       "      <td id=\"T_52283_row12_col5\" class=\"data row12 col5\" >6.98</td>\n",
       "      <td id=\"T_52283_row12_col6\" class=\"data row12 col6\" >14.40</td>\n",
       "      <td id=\"T_52283_row12_col7\" class=\"data row12 col7\" >9.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52283_level1_row13\" class=\"row_heading level1 row13\" >96</th>\n",
       "      <td id=\"T_52283_row13_col0\" class=\"data row13 col0\" >13.15</td>\n",
       "      <td id=\"T_52283_row13_col1\" class=\"data row13 col1\" >7.98</td>\n",
       "      <td id=\"T_52283_row13_col2\" class=\"data row13 col2\" >13.70</td>\n",
       "      <td id=\"T_52283_row13_col3\" class=\"data row13 col3\" >9.16</td>\n",
       "      <td id=\"T_52283_row13_col4\" class=\"data row13 col4\" >13.12</td>\n",
       "      <td id=\"T_52283_row13_col5\" class=\"data row13 col5\" >10.33</td>\n",
       "      <td id=\"T_52283_row13_col6\" class=\"data row13 col6\" >19.71</td>\n",
       "      <td id=\"T_52283_row13_col7\" class=\"data row13 col7\" >14.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_52283_level1_row14\" class=\"row_heading level1 row14\" >168</th>\n",
       "      <td id=\"T_52283_row14_col0\" class=\"data row14 col0\" >13.75</td>\n",
       "      <td id=\"T_52283_row14_col1\" class=\"data row14 col1\" >8.58</td>\n",
       "      <td id=\"T_52283_row14_col2\" class=\"data row14 col2\" >14.53</td>\n",
       "      <td id=\"T_52283_row14_col3\" class=\"data row14 col3\" >9.75</td>\n",
       "      <td id=\"T_52283_row14_col4\" class=\"data row14 col4\" >16.57</td>\n",
       "      <td id=\"T_52283_row14_col5\" class=\"data row14 col5\" >12.76</td>\n",
       "      <td id=\"T_52283_row14_col6\" class=\"data row14 col6\" >15.69</td>\n",
       "      <td id=\"T_52283_row14_col7\" class=\"data row14 col7\" >10.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f24fb6cd3d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read data\n",
    "naive_df = read_results_csv('naive/metrics_persistence_countries_minmax.csv',\n",
    "                            columns_to_extract = ('Persistence', ['RMSE', 'MAE'])\n",
    "                            )\n",
    "arima_df = read_results_csv('arima/metrics_arima_countries_minmax.csv',\n",
    "                             columns_to_extract = ('(S)ARIMA', ['RMSE', 'MAE'])\n",
    "                             )\n",
    "informer_df = read_results_csv('informer/informer.csv',\n",
    "                               columns_to_extract = ('Informer', ['RMSE', 'MAE'])\n",
    "                               )\n",
    "\n",
    "# Concat all dfs with default PatchTST\n",
    "all_df = pd.concat([informer_df, arima_df, naive_df], axis=1)\n",
    "\n",
    "# Scale\n",
    "scale = 10**2\n",
    "\n",
    "# Change country codes to country names\n",
    "level_names = all_df.index.names\n",
    "all_df.index = pd.MultiIndex.from_tuples(\n",
    "    [(map_country_name(x[0]), x[1]) for x in all_df.index], \n",
    "    names=level_names\n",
    ")\n",
    "all_df_scale = all_df * scale\n",
    "\n",
    "# Concat all dfs with best PatchTST - FINAL TABLE\n",
    "all_df_with_result = pd.concat(\n",
    "    [result_df, all_df_scale],\n",
    "    axis=1\n",
    ")\n",
    "\n",
    "unscaled_for_plots = all_df_with_result / scale\n",
    "\n",
    "all_df_with_result.columns.names = ['Model', 'Metrics']\n",
    "\n",
    "# Highlight the minimal values in bold.\n",
    "styled_df = style_dataframe(all_df_with_result, decimal_places=2, second_value=True)\n",
    "styled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save styled dataframe to HTML\n",
    "path = './stored_elements/tables'\n",
    "\n",
    "if not os.path.exists(path):\n",
    "    os.makedirs(path)\n",
    "styled_df.to_html(os.path.join(path, 'patchtst_baselines_10_power_2.html'), index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>PatchTST</th>\n",
       "      <th>Informer</th>\n",
       "      <th>(S)ARIMA</th>\n",
       "      <th>Persistence</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Pred_len</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Germany</th>\n",
       "      <th>24</th>\n",
       "      <td>-5.32</td>\n",
       "      <td>-5.34</td>\n",
       "      <td>-1.83</td>\n",
       "      <td>-5.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>-5.79</td>\n",
       "      <td>-5.71</td>\n",
       "      <td>-3.79</td>\n",
       "      <td>-6.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>-5.73</td>\n",
       "      <td>-6.03</td>\n",
       "      <td>-4.27</td>\n",
       "      <td>-6.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">United Kingdom</th>\n",
       "      <th>24</th>\n",
       "      <td>-5.38</td>\n",
       "      <td>-5.61</td>\n",
       "      <td>-2.39</td>\n",
       "      <td>-5.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>-5.93</td>\n",
       "      <td>-6.49</td>\n",
       "      <td>-3.84</td>\n",
       "      <td>-6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>-5.96</td>\n",
       "      <td>-7.62</td>\n",
       "      <td>-4.39</td>\n",
       "      <td>-6.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Spain</th>\n",
       "      <th>24</th>\n",
       "      <td>-3.79</td>\n",
       "      <td>-6.98</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-4.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>-4.69</td>\n",
       "      <td>-7.50</td>\n",
       "      <td>-2.95</td>\n",
       "      <td>-5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>-4.73</td>\n",
       "      <td>-7.30</td>\n",
       "      <td>-4.02</td>\n",
       "      <td>-5.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">France</th>\n",
       "      <th>24</th>\n",
       "      <td>-4.31</td>\n",
       "      <td>-4.54</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>-4.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>-5.42</td>\n",
       "      <td>-5.60</td>\n",
       "      <td>-2.74</td>\n",
       "      <td>-5.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>-5.39</td>\n",
       "      <td>-5.60</td>\n",
       "      <td>-3.39</td>\n",
       "      <td>-5.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Italy</th>\n",
       "      <th>24</th>\n",
       "      <td>-4.13</td>\n",
       "      <td>-4.07</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>-4.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>-5.18</td>\n",
       "      <td>-4.53</td>\n",
       "      <td>-2.79</td>\n",
       "      <td>-5.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>-5.17</td>\n",
       "      <td>-4.78</td>\n",
       "      <td>-3.81</td>\n",
       "      <td>-4.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Model                    PatchTST  Informer  (S)ARIMA  Persistence\n",
       "Country        Pred_len                                           \n",
       "Germany        24           -5.32     -5.34     -1.83        -5.74\n",
       "               96           -5.79     -5.71     -3.79        -6.45\n",
       "               168          -5.73     -6.03     -4.27        -6.38\n",
       "United Kingdom 24           -5.38     -5.61     -2.39        -5.44\n",
       "               96           -5.93     -6.49     -3.84        -6.12\n",
       "               168          -5.96     -7.62     -4.39        -6.14\n",
       "Spain          24           -3.79     -6.98     -1.79        -4.63\n",
       "               96           -4.69     -7.50     -2.95        -5.75\n",
       "               168          -4.73     -7.30     -4.02        -5.36\n",
       "France         24           -4.31     -4.54     -1.55        -4.24\n",
       "               96           -5.42     -5.60     -2.74        -5.32\n",
       "               168          -5.39     -5.60     -3.39        -5.48\n",
       "Italy          24           -4.13     -4.07     -1.67        -4.73\n",
       "               96           -5.18     -4.53     -2.79        -5.61\n",
       "               168          -5.17     -4.78     -3.81        -4.77"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Absolute difference between MAE and RMSE\n",
    "maes = all_df_with_result.loc[:, (slice(None), 'MAE')].droplevel('Metrics', axis=1)\n",
    "rmses = all_df_with_result.loc[:, (slice(None), 'RMSE')].droplevel('Metrics', axis=1)\n",
    "(maes - rmses).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th colspan=\"2\" halign=\"left\">PatchTST</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Informer</th>\n",
       "      <th colspan=\"2\" halign=\"left\">(S)ARIMA</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Persistence</th>\n",
       "      <th>rmse_improvement</th>\n",
       "      <th>mae_improvement</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Metrics</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Pred_len</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Germany</th>\n",
       "      <th>24</th>\n",
       "      <td>14.43</td>\n",
       "      <td>9.11</td>\n",
       "      <td>15.97</td>\n",
       "      <td>10.63</td>\n",
       "      <td>11.15</td>\n",
       "      <td>9.32</td>\n",
       "      <td>19.61</td>\n",
       "      <td>13.87</td>\n",
       "      <td>9.64</td>\n",
       "      <td>14.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>18.61</td>\n",
       "      <td>12.82</td>\n",
       "      <td>20.39</td>\n",
       "      <td>14.68</td>\n",
       "      <td>20.28</td>\n",
       "      <td>16.49</td>\n",
       "      <td>26.14</td>\n",
       "      <td>19.69</td>\n",
       "      <td>8.74</td>\n",
       "      <td>12.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>19.27</td>\n",
       "      <td>13.54</td>\n",
       "      <td>21.42</td>\n",
       "      <td>15.39</td>\n",
       "      <td>22.79</td>\n",
       "      <td>18.51</td>\n",
       "      <td>23.39</td>\n",
       "      <td>17.01</td>\n",
       "      <td>10.06</td>\n",
       "      <td>12.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">United Kingdom</th>\n",
       "      <th>24</th>\n",
       "      <td>15.65</td>\n",
       "      <td>10.27</td>\n",
       "      <td>17.64</td>\n",
       "      <td>12.03</td>\n",
       "      <td>14.41</td>\n",
       "      <td>12.01</td>\n",
       "      <td>19.89</td>\n",
       "      <td>14.45</td>\n",
       "      <td>11.31</td>\n",
       "      <td>14.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>20.02</td>\n",
       "      <td>14.09</td>\n",
       "      <td>22.50</td>\n",
       "      <td>16.01</td>\n",
       "      <td>21.53</td>\n",
       "      <td>17.69</td>\n",
       "      <td>25.54</td>\n",
       "      <td>19.42</td>\n",
       "      <td>11.02</td>\n",
       "      <td>11.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>20.62</td>\n",
       "      <td>14.66</td>\n",
       "      <td>25.91</td>\n",
       "      <td>18.29</td>\n",
       "      <td>22.67</td>\n",
       "      <td>18.28</td>\n",
       "      <td>24.69</td>\n",
       "      <td>18.56</td>\n",
       "      <td>20.43</td>\n",
       "      <td>19.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Spain</th>\n",
       "      <th>24</th>\n",
       "      <td>9.87</td>\n",
       "      <td>6.07</td>\n",
       "      <td>17.42</td>\n",
       "      <td>10.45</td>\n",
       "      <td>9.68</td>\n",
       "      <td>7.89</td>\n",
       "      <td>14.06</td>\n",
       "      <td>9.43</td>\n",
       "      <td>43.38</td>\n",
       "      <td>41.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.49</td>\n",
       "      <td>8.79</td>\n",
       "      <td>21.24</td>\n",
       "      <td>13.74</td>\n",
       "      <td>14.17</td>\n",
       "      <td>11.22</td>\n",
       "      <td>19.67</td>\n",
       "      <td>13.92</td>\n",
       "      <td>36.49</td>\n",
       "      <td>35.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>14.21</td>\n",
       "      <td>9.48</td>\n",
       "      <td>21.38</td>\n",
       "      <td>14.08</td>\n",
       "      <td>17.61</td>\n",
       "      <td>13.59</td>\n",
       "      <td>17.74</td>\n",
       "      <td>12.38</td>\n",
       "      <td>33.52</td>\n",
       "      <td>32.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">France</th>\n",
       "      <th>24</th>\n",
       "      <td>9.97</td>\n",
       "      <td>5.66</td>\n",
       "      <td>11.71</td>\n",
       "      <td>7.17</td>\n",
       "      <td>7.60</td>\n",
       "      <td>6.06</td>\n",
       "      <td>12.24</td>\n",
       "      <td>8.00</td>\n",
       "      <td>14.86</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.63</td>\n",
       "      <td>8.20</td>\n",
       "      <td>14.80</td>\n",
       "      <td>9.20</td>\n",
       "      <td>12.54</td>\n",
       "      <td>9.81</td>\n",
       "      <td>17.82</td>\n",
       "      <td>12.50</td>\n",
       "      <td>7.94</td>\n",
       "      <td>10.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>14.26</td>\n",
       "      <td>8.86</td>\n",
       "      <td>15.14</td>\n",
       "      <td>9.53</td>\n",
       "      <td>14.89</td>\n",
       "      <td>11.50</td>\n",
       "      <td>16.29</td>\n",
       "      <td>10.81</td>\n",
       "      <td>5.82</td>\n",
       "      <td>7.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Italy</th>\n",
       "      <th>24</th>\n",
       "      <td>9.95</td>\n",
       "      <td>5.82</td>\n",
       "      <td>10.67</td>\n",
       "      <td>6.60</td>\n",
       "      <td>8.64</td>\n",
       "      <td>6.98</td>\n",
       "      <td>14.40</td>\n",
       "      <td>9.66</td>\n",
       "      <td>6.77</td>\n",
       "      <td>11.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.15</td>\n",
       "      <td>7.98</td>\n",
       "      <td>13.70</td>\n",
       "      <td>9.16</td>\n",
       "      <td>13.12</td>\n",
       "      <td>10.33</td>\n",
       "      <td>19.71</td>\n",
       "      <td>14.10</td>\n",
       "      <td>3.98</td>\n",
       "      <td>12.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>13.75</td>\n",
       "      <td>8.58</td>\n",
       "      <td>14.53</td>\n",
       "      <td>9.75</td>\n",
       "      <td>16.57</td>\n",
       "      <td>12.76</td>\n",
       "      <td>15.69</td>\n",
       "      <td>10.92</td>\n",
       "      <td>5.34</td>\n",
       "      <td>11.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Model                   PatchTST        Informer        (S)ARIMA         \\\n",
       "Metrics                     RMSE    MAE     RMSE    MAE     RMSE    MAE   \n",
       "Country        Pred_len                                                   \n",
       "Germany        24          14.43   9.11    15.97  10.63    11.15   9.32   \n",
       "               96          18.61  12.82    20.39  14.68    20.28  16.49   \n",
       "               168         19.27  13.54    21.42  15.39    22.79  18.51   \n",
       "United Kingdom 24          15.65  10.27    17.64  12.03    14.41  12.01   \n",
       "               96          20.02  14.09    22.50  16.01    21.53  17.69   \n",
       "               168         20.62  14.66    25.91  18.29    22.67  18.28   \n",
       "Spain          24           9.87   6.07    17.42  10.45     9.68   7.89   \n",
       "               96          13.49   8.79    21.24  13.74    14.17  11.22   \n",
       "               168         14.21   9.48    21.38  14.08    17.61  13.59   \n",
       "France         24           9.97   5.66    11.71   7.17     7.60   6.06   \n",
       "               96          13.63   8.20    14.80   9.20    12.54   9.81   \n",
       "               168         14.26   8.86    15.14   9.53    14.89  11.50   \n",
       "Italy          24           9.95   5.82    10.67   6.60     8.64   6.98   \n",
       "               96          13.15   7.98    13.70   9.16    13.12  10.33   \n",
       "               168         13.75   8.58    14.53   9.75    16.57  12.76   \n",
       "\n",
       "Model                   Persistence        rmse_improvement mae_improvement  \n",
       "Metrics                        RMSE    MAE                                   \n",
       "Country        Pred_len                                                      \n",
       "Germany        24             19.61  13.87             9.64           14.32  \n",
       "               96             26.14  19.69             8.74           12.67  \n",
       "               168            23.39  17.01            10.06           12.03  \n",
       "United Kingdom 24             19.89  14.45            11.31           14.64  \n",
       "               96             25.54  19.42            11.02           11.95  \n",
       "               168            24.69  18.56            20.43           19.88  \n",
       "Spain          24             14.06   9.43            43.38           41.88  \n",
       "               96             19.67  13.92            36.49           35.98  \n",
       "               168            17.74  12.38            33.52           32.68  \n",
       "France         24             12.24   8.00            14.86           21.00  \n",
       "               96             17.82  12.50             7.94           10.86  \n",
       "               168            16.29  10.81             5.82            7.06  \n",
       "Italy          24             14.40   9.66             6.77           11.81  \n",
       "               96             19.71  14.10             3.98           12.98  \n",
       "               168            15.69  10.92             5.34           11.98  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "informer_improvement = all_df_with_result.copy()\n",
    "informer_improvement = calculate_improvement(informer_improvement, \n",
    "                      base_mae_model='PatchTST', \n",
    "                      model_to_compare_mae='Informer',\n",
    "                      grouped_by_models=False).round(2)\n",
    "informer_improvement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "France            12.973333\n",
       "Germany           13.006667\n",
       "Italy             12.256667\n",
       "Spain             36.846667\n",
       "United Kingdom    15.490000\n",
       "Name: mae_improvement, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "informer_improvement['mae_improvement'].groupby('Country').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "France             9.540000\n",
       "Germany            9.480000\n",
       "Italy              5.363333\n",
       "Spain             37.796667\n",
       "United Kingdom    14.253333\n",
       "Name: rmse_improvement, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "informer_improvement['rmse_improvement'].groupby('Country').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average improvement of MAE: 18.11 %.\n",
      "Average improvement of RMSE: 15.29 %.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Average improvement of MAE: {(informer_improvement.loc[informer_improvement['mae_improvement']!=0]['mae_improvement'].mean()).round(2)} %.\")\n",
    "print(f\"Average improvement of RMSE: {(informer_improvement.loc[informer_improvement['rmse_improvement']!=0]['rmse_improvement'].mean()).round(2)} %.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th colspan=\"2\" halign=\"left\">PatchTST</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Informer</th>\n",
       "      <th colspan=\"2\" halign=\"left\">(S)ARIMA</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Persistence</th>\n",
       "      <th>improvement_rmse</th>\n",
       "      <th>improvement_mae</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Metrics</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Pred_len</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Germany</th>\n",
       "      <th>24</th>\n",
       "      <td>14.43</td>\n",
       "      <td>9.11</td>\n",
       "      <td>15.97</td>\n",
       "      <td>10.63</td>\n",
       "      <td>11.15</td>\n",
       "      <td>9.32</td>\n",
       "      <td>19.61</td>\n",
       "      <td>13.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>18.61</td>\n",
       "      <td>12.82</td>\n",
       "      <td>20.39</td>\n",
       "      <td>14.68</td>\n",
       "      <td>20.28</td>\n",
       "      <td>16.49</td>\n",
       "      <td>26.14</td>\n",
       "      <td>19.69</td>\n",
       "      <td>8.26</td>\n",
       "      <td>12.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>19.27</td>\n",
       "      <td>13.54</td>\n",
       "      <td>21.42</td>\n",
       "      <td>15.39</td>\n",
       "      <td>22.79</td>\n",
       "      <td>18.51</td>\n",
       "      <td>23.39</td>\n",
       "      <td>17.01</td>\n",
       "      <td>10.06</td>\n",
       "      <td>12.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">United Kingdom</th>\n",
       "      <th>24</th>\n",
       "      <td>15.65</td>\n",
       "      <td>10.27</td>\n",
       "      <td>17.64</td>\n",
       "      <td>12.03</td>\n",
       "      <td>14.41</td>\n",
       "      <td>12.01</td>\n",
       "      <td>19.89</td>\n",
       "      <td>14.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>20.02</td>\n",
       "      <td>14.09</td>\n",
       "      <td>22.50</td>\n",
       "      <td>16.01</td>\n",
       "      <td>21.53</td>\n",
       "      <td>17.69</td>\n",
       "      <td>25.54</td>\n",
       "      <td>19.42</td>\n",
       "      <td>7.00</td>\n",
       "      <td>11.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>20.62</td>\n",
       "      <td>14.66</td>\n",
       "      <td>25.91</td>\n",
       "      <td>18.29</td>\n",
       "      <td>22.67</td>\n",
       "      <td>18.28</td>\n",
       "      <td>24.69</td>\n",
       "      <td>18.56</td>\n",
       "      <td>9.04</td>\n",
       "      <td>19.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Spain</th>\n",
       "      <th>24</th>\n",
       "      <td>9.87</td>\n",
       "      <td>6.07</td>\n",
       "      <td>17.42</td>\n",
       "      <td>10.45</td>\n",
       "      <td>9.68</td>\n",
       "      <td>7.89</td>\n",
       "      <td>14.06</td>\n",
       "      <td>9.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.49</td>\n",
       "      <td>8.79</td>\n",
       "      <td>21.24</td>\n",
       "      <td>13.74</td>\n",
       "      <td>14.17</td>\n",
       "      <td>11.22</td>\n",
       "      <td>19.67</td>\n",
       "      <td>13.92</td>\n",
       "      <td>4.82</td>\n",
       "      <td>21.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>14.21</td>\n",
       "      <td>9.48</td>\n",
       "      <td>21.38</td>\n",
       "      <td>14.08</td>\n",
       "      <td>17.61</td>\n",
       "      <td>13.59</td>\n",
       "      <td>17.74</td>\n",
       "      <td>12.38</td>\n",
       "      <td>19.31</td>\n",
       "      <td>23.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">France</th>\n",
       "      <th>24</th>\n",
       "      <td>9.97</td>\n",
       "      <td>5.66</td>\n",
       "      <td>11.71</td>\n",
       "      <td>7.17</td>\n",
       "      <td>7.60</td>\n",
       "      <td>6.06</td>\n",
       "      <td>12.24</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.63</td>\n",
       "      <td>8.20</td>\n",
       "      <td>14.80</td>\n",
       "      <td>9.20</td>\n",
       "      <td>12.54</td>\n",
       "      <td>9.81</td>\n",
       "      <td>17.82</td>\n",
       "      <td>12.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>14.26</td>\n",
       "      <td>8.86</td>\n",
       "      <td>15.14</td>\n",
       "      <td>9.53</td>\n",
       "      <td>14.89</td>\n",
       "      <td>11.50</td>\n",
       "      <td>16.29</td>\n",
       "      <td>10.81</td>\n",
       "      <td>4.26</td>\n",
       "      <td>7.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Italy</th>\n",
       "      <th>24</th>\n",
       "      <td>9.95</td>\n",
       "      <td>5.82</td>\n",
       "      <td>10.67</td>\n",
       "      <td>6.60</td>\n",
       "      <td>8.64</td>\n",
       "      <td>6.98</td>\n",
       "      <td>14.40</td>\n",
       "      <td>9.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.15</td>\n",
       "      <td>7.98</td>\n",
       "      <td>13.70</td>\n",
       "      <td>9.16</td>\n",
       "      <td>13.12</td>\n",
       "      <td>10.33</td>\n",
       "      <td>19.71</td>\n",
       "      <td>14.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>13.75</td>\n",
       "      <td>8.58</td>\n",
       "      <td>14.53</td>\n",
       "      <td>9.75</td>\n",
       "      <td>16.57</td>\n",
       "      <td>12.76</td>\n",
       "      <td>15.69</td>\n",
       "      <td>10.92</td>\n",
       "      <td>5.34</td>\n",
       "      <td>11.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Model                   PatchTST        Informer        (S)ARIMA         \\\n",
       "Metrics                     RMSE    MAE     RMSE    MAE     RMSE    MAE   \n",
       "Country        Pred_len                                                   \n",
       "Germany        24          14.43   9.11    15.97  10.63    11.15   9.32   \n",
       "               96          18.61  12.82    20.39  14.68    20.28  16.49   \n",
       "               168         19.27  13.54    21.42  15.39    22.79  18.51   \n",
       "United Kingdom 24          15.65  10.27    17.64  12.03    14.41  12.01   \n",
       "               96          20.02  14.09    22.50  16.01    21.53  17.69   \n",
       "               168         20.62  14.66    25.91  18.29    22.67  18.28   \n",
       "Spain          24           9.87   6.07    17.42  10.45     9.68   7.89   \n",
       "               96          13.49   8.79    21.24  13.74    14.17  11.22   \n",
       "               168         14.21   9.48    21.38  14.08    17.61  13.59   \n",
       "France         24           9.97   5.66    11.71   7.17     7.60   6.06   \n",
       "               96          13.63   8.20    14.80   9.20    12.54   9.81   \n",
       "               168         14.26   8.86    15.14   9.53    14.89  11.50   \n",
       "Italy          24           9.95   5.82    10.67   6.60     8.64   6.98   \n",
       "               96          13.15   7.98    13.70   9.16    13.12  10.33   \n",
       "               168         13.75   8.58    14.53   9.75    16.57  12.76   \n",
       "\n",
       "Model                   Persistence        improvement_rmse improvement_mae  \n",
       "Metrics                        RMSE    MAE                                   \n",
       "Country        Pred_len                                                      \n",
       "Germany        24             19.61  13.87             0.00            2.26  \n",
       "               96             26.14  19.69             8.26           12.67  \n",
       "               168            23.39  17.01            10.06           12.03  \n",
       "United Kingdom 24             19.89  14.45             0.00           14.53  \n",
       "               96             25.54  19.42             7.00           11.95  \n",
       "               168            24.69  18.56             9.04           19.83  \n",
       "Spain          24             14.06   9.43             0.00           23.02  \n",
       "               96             19.67  13.92             4.82           21.61  \n",
       "               168            17.74  12.38            19.31           23.45  \n",
       "France         24             12.24   8.00             0.00            6.48  \n",
       "               96             17.82  12.50             0.00           10.86  \n",
       "               168            16.29  10.81             4.26            7.06  \n",
       "Italy          24             14.40   9.66             0.00           11.81  \n",
       "               96             19.71  14.10             0.00           12.98  \n",
       "               168            15.69  10.92             5.34           11.98  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second_best_improvement = all_df_with_result.copy()\n",
    "second_best_improvement = calculate_improvement(second_best_improvement, \n",
    "                      base_mae_model='PatchTST', \n",
    "                      grouped_by_models=False).round(2)\n",
    "second_best_improvement\n",
    "                                                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average improvement of MAE: 13.5 %.\n",
      "Average improvement of RMSE: 8.51 %.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Average improvement of MAE: {(second_best_improvement.loc[second_best_improvement['improvement_mae']!=0]['improvement_mae'].mean()).round(2)} %.\")\n",
    "print(f\"Average improvement of RMSE: {(second_best_improvement.loc[second_best_improvement['improvement_rmse']!=0]['improvement_rmse'].mean()).round(2)} %.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "France             8.133333\n",
       "Germany            8.986667\n",
       "Italy             12.256667\n",
       "Spain             22.693333\n",
       "United Kingdom    15.436667\n",
       "Name: improvement_mae, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second_best_improvement['improvement_mae'].groupby('Country').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "France            1.420000\n",
       "Germany           6.106667\n",
       "Italy             1.780000\n",
       "Spain             8.043333\n",
       "United Kingdom    5.346667\n",
       "Name: improvement_rmse, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second_best_improvement['improvement_rmse'].groupby('Country').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pred_len\n",
       "24     11.620\n",
       "96     14.014\n",
       "168    14.870\n",
       "Name: improvement_mae, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second_best_improvement['improvement_mae'].groupby('Pred_len').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results_models_multiple_countries(unscaled_for_plots, \n",
    "                                       function=\"lines\", \n",
    "                                       eval_metric='MAE',\n",
    "                                       decimal_places=2\n",
    "                                       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results_models_multiple_countries(unscaled_for_plots, \n",
    "                                       function=\"lines\", \n",
    "                                       eval_metric='RMSE',\n",
    "                                       decimal_places=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Self-supervised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_ce310_row0_col2, #T_ce310_row0_col3, #T_ce310_row1_col2, #T_ce310_row1_col3, #T_ce310_row2_col0, #T_ce310_row2_col3, #T_ce310_row3_col2, #T_ce310_row3_col3, #T_ce310_row4_col2, #T_ce310_row4_col3, #T_ce310_row5_col2, #T_ce310_row5_col3, #T_ce310_row6_col0, #T_ce310_row6_col1, #T_ce310_row7_col0, #T_ce310_row7_col3, #T_ce310_row8_col2, #T_ce310_row8_col3, #T_ce310_row9_col0, #T_ce310_row9_col1, #T_ce310_row10_col1, #T_ce310_row10_col2, #T_ce310_row11_col2, #T_ce310_row11_col3, #T_ce310_row12_col0, #T_ce310_row12_col3, #T_ce310_row13_col2, #T_ce310_row13_col3, #T_ce310_row14_col2, #T_ce310_row14_col3 {\n",
       "  font-weight: bold;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_ce310\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"index_name level0\" >Model</th>\n",
       "      <th id=\"T_ce310_level0_col0\" class=\"col_heading level0 col0\" colspan=\"2\">Self-supervised</th>\n",
       "      <th id=\"T_ce310_level0_col2\" class=\"col_heading level0 col2\" colspan=\"2\">PatchTST</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"index_name level1\" >Metrics</th>\n",
       "      <th id=\"T_ce310_level1_col0\" class=\"col_heading level1 col0\" >RMSE</th>\n",
       "      <th id=\"T_ce310_level1_col1\" class=\"col_heading level1 col1\" >MAE</th>\n",
       "      <th id=\"T_ce310_level1_col2\" class=\"col_heading level1 col2\" >RMSE</th>\n",
       "      <th id=\"T_ce310_level1_col3\" class=\"col_heading level1 col3\" >MAE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Country</th>\n",
       "      <th class=\"index_name level1\" >Pred_len</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ce310_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"3\">Germany</th>\n",
       "      <th id=\"T_ce310_level1_row0\" class=\"row_heading level1 row0\" >24</th>\n",
       "      <td id=\"T_ce310_row0_col0\" class=\"data row0 col0\" >14.43</td>\n",
       "      <td id=\"T_ce310_row0_col1\" class=\"data row0 col1\" >9.22</td>\n",
       "      <td id=\"T_ce310_row0_col2\" class=\"data row0 col2\" >14.43</td>\n",
       "      <td id=\"T_ce310_row0_col3\" class=\"data row0 col3\" >9.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce310_level1_row1\" class=\"row_heading level1 row1\" >96</th>\n",
       "      <td id=\"T_ce310_row1_col0\" class=\"data row1 col0\" >18.75</td>\n",
       "      <td id=\"T_ce310_row1_col1\" class=\"data row1 col1\" >13.02</td>\n",
       "      <td id=\"T_ce310_row1_col2\" class=\"data row1 col2\" >18.61</td>\n",
       "      <td id=\"T_ce310_row1_col3\" class=\"data row1 col3\" >12.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce310_level1_row2\" class=\"row_heading level1 row2\" >168</th>\n",
       "      <td id=\"T_ce310_row2_col0\" class=\"data row2 col0\" >19.23</td>\n",
       "      <td id=\"T_ce310_row2_col1\" class=\"data row2 col1\" >13.59</td>\n",
       "      <td id=\"T_ce310_row2_col2\" class=\"data row2 col2\" >19.27</td>\n",
       "      <td id=\"T_ce310_row2_col3\" class=\"data row2 col3\" >13.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce310_level0_row3\" class=\"row_heading level0 row3\" rowspan=\"3\">United Kingdom</th>\n",
       "      <th id=\"T_ce310_level1_row3\" class=\"row_heading level1 row3\" >24</th>\n",
       "      <td id=\"T_ce310_row3_col0\" class=\"data row3 col0\" >15.75</td>\n",
       "      <td id=\"T_ce310_row3_col1\" class=\"data row3 col1\" >10.47</td>\n",
       "      <td id=\"T_ce310_row3_col2\" class=\"data row3 col2\" >15.65</td>\n",
       "      <td id=\"T_ce310_row3_col3\" class=\"data row3 col3\" >10.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce310_level1_row4\" class=\"row_heading level1 row4\" >96</th>\n",
       "      <td id=\"T_ce310_row4_col0\" class=\"data row4 col0\" >20.05</td>\n",
       "      <td id=\"T_ce310_row4_col1\" class=\"data row4 col1\" >14.17</td>\n",
       "      <td id=\"T_ce310_row4_col2\" class=\"data row4 col2\" >20.02</td>\n",
       "      <td id=\"T_ce310_row4_col3\" class=\"data row4 col3\" >14.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce310_level1_row5\" class=\"row_heading level1 row5\" >168</th>\n",
       "      <td id=\"T_ce310_row5_col0\" class=\"data row5 col0\" >20.67</td>\n",
       "      <td id=\"T_ce310_row5_col1\" class=\"data row5 col1\" >14.78</td>\n",
       "      <td id=\"T_ce310_row5_col2\" class=\"data row5 col2\" >20.62</td>\n",
       "      <td id=\"T_ce310_row5_col3\" class=\"data row5 col3\" >14.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce310_level0_row6\" class=\"row_heading level0 row6\" rowspan=\"3\">Spain</th>\n",
       "      <th id=\"T_ce310_level1_row6\" class=\"row_heading level1 row6\" >24</th>\n",
       "      <td id=\"T_ce310_row6_col0\" class=\"data row6 col0\" >9.75</td>\n",
       "      <td id=\"T_ce310_row6_col1\" class=\"data row6 col1\" >6.04</td>\n",
       "      <td id=\"T_ce310_row6_col2\" class=\"data row6 col2\" >9.87</td>\n",
       "      <td id=\"T_ce310_row6_col3\" class=\"data row6 col3\" >6.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce310_level1_row7\" class=\"row_heading level1 row7\" >96</th>\n",
       "      <td id=\"T_ce310_row7_col0\" class=\"data row7 col0\" >13.45</td>\n",
       "      <td id=\"T_ce310_row7_col1\" class=\"data row7 col1\" >8.88</td>\n",
       "      <td id=\"T_ce310_row7_col2\" class=\"data row7 col2\" >13.49</td>\n",
       "      <td id=\"T_ce310_row7_col3\" class=\"data row7 col3\" >8.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce310_level1_row8\" class=\"row_heading level1 row8\" >168</th>\n",
       "      <td id=\"T_ce310_row8_col0\" class=\"data row8 col0\" >14.29</td>\n",
       "      <td id=\"T_ce310_row8_col1\" class=\"data row8 col1\" >9.48</td>\n",
       "      <td id=\"T_ce310_row8_col2\" class=\"data row8 col2\" >14.21</td>\n",
       "      <td id=\"T_ce310_row8_col3\" class=\"data row8 col3\" >9.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce310_level0_row9\" class=\"row_heading level0 row9\" rowspan=\"3\">France</th>\n",
       "      <th id=\"T_ce310_level1_row9\" class=\"row_heading level1 row9\" >24</th>\n",
       "      <td id=\"T_ce310_row9_col0\" class=\"data row9 col0\" >9.84</td>\n",
       "      <td id=\"T_ce310_row9_col1\" class=\"data row9 col1\" >5.57</td>\n",
       "      <td id=\"T_ce310_row9_col2\" class=\"data row9 col2\" >9.97</td>\n",
       "      <td id=\"T_ce310_row9_col3\" class=\"data row9 col3\" >5.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce310_level1_row10\" class=\"row_heading level1 row10\" >96</th>\n",
       "      <td id=\"T_ce310_row10_col0\" class=\"data row10 col0\" >13.66</td>\n",
       "      <td id=\"T_ce310_row10_col1\" class=\"data row10 col1\" >8.20</td>\n",
       "      <td id=\"T_ce310_row10_col2\" class=\"data row10 col2\" >13.63</td>\n",
       "      <td id=\"T_ce310_row10_col3\" class=\"data row10 col3\" >8.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce310_level1_row11\" class=\"row_heading level1 row11\" >168</th>\n",
       "      <td id=\"T_ce310_row11_col0\" class=\"data row11 col0\" >14.46</td>\n",
       "      <td id=\"T_ce310_row11_col1\" class=\"data row11 col1\" >9.10</td>\n",
       "      <td id=\"T_ce310_row11_col2\" class=\"data row11 col2\" >14.26</td>\n",
       "      <td id=\"T_ce310_row11_col3\" class=\"data row11 col3\" >8.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce310_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"3\">Italy</th>\n",
       "      <th id=\"T_ce310_level1_row12\" class=\"row_heading level1 row12\" >24</th>\n",
       "      <td id=\"T_ce310_row12_col0\" class=\"data row12 col0\" >9.91</td>\n",
       "      <td id=\"T_ce310_row12_col1\" class=\"data row12 col1\" >5.91</td>\n",
       "      <td id=\"T_ce310_row12_col2\" class=\"data row12 col2\" >9.95</td>\n",
       "      <td id=\"T_ce310_row12_col3\" class=\"data row12 col3\" >5.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce310_level1_row13\" class=\"row_heading level1 row13\" >96</th>\n",
       "      <td id=\"T_ce310_row13_col0\" class=\"data row13 col0\" >13.33</td>\n",
       "      <td id=\"T_ce310_row13_col1\" class=\"data row13 col1\" >8.23</td>\n",
       "      <td id=\"T_ce310_row13_col2\" class=\"data row13 col2\" >13.15</td>\n",
       "      <td id=\"T_ce310_row13_col3\" class=\"data row13 col3\" >7.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce310_level1_row14\" class=\"row_heading level1 row14\" >168</th>\n",
       "      <td id=\"T_ce310_row14_col0\" class=\"data row14 col0\" >13.83</td>\n",
       "      <td id=\"T_ce310_row14_col1\" class=\"data row14 col1\" >8.79</td>\n",
       "      <td id=\"T_ce310_row14_col2\" class=\"data row14 col2\" >13.75</td>\n",
       "      <td id=\"T_ce310_row14_col3\" class=\"data row14 col3\" >8.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f24fb395710>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read data\n",
    "self_sup = read_results_csv('patchtst/finetune_results.csv',\n",
    "                                    columns_to_extract = ('Self-supervised', ['RMSE', 'MAE'])\n",
    "                                    )\n",
    "self_supervised = self_sup.copy()\n",
    "# Change country codes to country names\n",
    "self_supervised.index = pd.MultiIndex.from_tuples(\n",
    "    [(map_country_name(x[0]), x[1]) for x in self_supervised.index],\n",
    "    names=level_names\n",
    ")\n",
    "\n",
    "all_df = pd.concat([self_supervised, result_df_unscaled], axis=1)\n",
    "\n",
    "# Scale\n",
    "all_df_scale = all_df * scale\n",
    "\n",
    "# Highlight the minimal values in bold\n",
    "styled_df = style_dataframe(all_df_scale, decimal_places=2)\n",
    "styled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "styled_df.to_html(os.path.join(path, 'self_supervised_10_power_2.html'), index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Ablation study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_4bbac_row0_col4, #T_4bbac_row0_col5, #T_4bbac_row1_col4, #T_4bbac_row1_col5, #T_4bbac_row2_col4, #T_4bbac_row2_col5, #T_4bbac_row3_col4, #T_4bbac_row3_col5, #T_4bbac_row4_col4, #T_4bbac_row4_col5, #T_4bbac_row5_col2, #T_4bbac_row5_col3, #T_4bbac_row6_col2, #T_4bbac_row6_col3, #T_4bbac_row7_col2, #T_4bbac_row7_col3, #T_4bbac_row8_col2, #T_4bbac_row8_col3, #T_4bbac_row9_col3, #T_4bbac_row9_col4, #T_4bbac_row10_col3, #T_4bbac_row10_col6, #T_4bbac_row11_col3, #T_4bbac_row11_col6, #T_4bbac_row12_col2, #T_4bbac_row12_col3, #T_4bbac_row13_col4, #T_4bbac_row13_col5, #T_4bbac_row14_col4, #T_4bbac_row14_col5 {\n",
       "  font-weight: bold;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4bbac\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"index_name level0\" >Model</th>\n",
       "      <th id=\"T_4bbac_level0_col0\" class=\"col_heading level0 col0\" colspan=\"2\">PatchTST</th>\n",
       "      <th id=\"T_4bbac_level0_col2\" class=\"col_heading level0 col2\" colspan=\"2\">-RevIN</th>\n",
       "      <th id=\"T_4bbac_level0_col4\" class=\"col_heading level0 col4\" colspan=\"2\">CM</th>\n",
       "      <th id=\"T_4bbac_level0_col6\" class=\"col_heading level0 col6\" colspan=\"2\">-P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"index_name level1\" >Metrics</th>\n",
       "      <th id=\"T_4bbac_level1_col0\" class=\"col_heading level1 col0\" >RMSE</th>\n",
       "      <th id=\"T_4bbac_level1_col1\" class=\"col_heading level1 col1\" >MAE</th>\n",
       "      <th id=\"T_4bbac_level1_col2\" class=\"col_heading level1 col2\" >RMSE</th>\n",
       "      <th id=\"T_4bbac_level1_col3\" class=\"col_heading level1 col3\" >MAE</th>\n",
       "      <th id=\"T_4bbac_level1_col4\" class=\"col_heading level1 col4\" >RMSE</th>\n",
       "      <th id=\"T_4bbac_level1_col5\" class=\"col_heading level1 col5\" >MAE</th>\n",
       "      <th id=\"T_4bbac_level1_col6\" class=\"col_heading level1 col6\" >RMSE</th>\n",
       "      <th id=\"T_4bbac_level1_col7\" class=\"col_heading level1 col7\" >MAE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Country</th>\n",
       "      <th class=\"index_name level1\" >Pred_len</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4bbac_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"3\">Germany</th>\n",
       "      <th id=\"T_4bbac_level1_row0\" class=\"row_heading level1 row0\" >24</th>\n",
       "      <td id=\"T_4bbac_row0_col0\" class=\"data row0 col0\" >14.43</td>\n",
       "      <td id=\"T_4bbac_row0_col1\" class=\"data row0 col1\" >9.11</td>\n",
       "      <td id=\"T_4bbac_row0_col2\" class=\"data row0 col2\" >14.77</td>\n",
       "      <td id=\"T_4bbac_row0_col3\" class=\"data row0 col3\" >9.52</td>\n",
       "      <td id=\"T_4bbac_row0_col4\" class=\"data row0 col4\" >15.96</td>\n",
       "      <td id=\"T_4bbac_row0_col5\" class=\"data row0 col5\" >10.60</td>\n",
       "      <td id=\"T_4bbac_row0_col6\" class=\"data row0 col6\" >14.80</td>\n",
       "      <td id=\"T_4bbac_row0_col7\" class=\"data row0 col7\" >9.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bbac_level1_row1\" class=\"row_heading level1 row1\" >96</th>\n",
       "      <td id=\"T_4bbac_row1_col0\" class=\"data row1 col0\" >18.61</td>\n",
       "      <td id=\"T_4bbac_row1_col1\" class=\"data row1 col1\" >12.82</td>\n",
       "      <td id=\"T_4bbac_row1_col2\" class=\"data row1 col2\" >19.06</td>\n",
       "      <td id=\"T_4bbac_row1_col3\" class=\"data row1 col3\" >13.06</td>\n",
       "      <td id=\"T_4bbac_row1_col4\" class=\"data row1 col4\" >19.53</td>\n",
       "      <td id=\"T_4bbac_row1_col5\" class=\"data row1 col5\" >13.84</td>\n",
       "      <td id=\"T_4bbac_row1_col6\" class=\"data row1 col6\" >18.82</td>\n",
       "      <td id=\"T_4bbac_row1_col7\" class=\"data row1 col7\" >13.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bbac_level1_row2\" class=\"row_heading level1 row2\" >168</th>\n",
       "      <td id=\"T_4bbac_row2_col0\" class=\"data row2 col0\" >19.27</td>\n",
       "      <td id=\"T_4bbac_row2_col1\" class=\"data row2 col1\" >13.54</td>\n",
       "      <td id=\"T_4bbac_row2_col2\" class=\"data row2 col2\" >19.75</td>\n",
       "      <td id=\"T_4bbac_row2_col3\" class=\"data row2 col3\" >13.73</td>\n",
       "      <td id=\"T_4bbac_row2_col4\" class=\"data row2 col4\" >20.01</td>\n",
       "      <td id=\"T_4bbac_row2_col5\" class=\"data row2 col5\" >14.28</td>\n",
       "      <td id=\"T_4bbac_row2_col6\" class=\"data row2 col6\" >19.39</td>\n",
       "      <td id=\"T_4bbac_row2_col7\" class=\"data row2 col7\" >13.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bbac_level0_row3\" class=\"row_heading level0 row3\" rowspan=\"3\">United Kingdom</th>\n",
       "      <th id=\"T_4bbac_level1_row3\" class=\"row_heading level1 row3\" >24</th>\n",
       "      <td id=\"T_4bbac_row3_col0\" class=\"data row3 col0\" >15.65</td>\n",
       "      <td id=\"T_4bbac_row3_col1\" class=\"data row3 col1\" >10.27</td>\n",
       "      <td id=\"T_4bbac_row3_col2\" class=\"data row3 col2\" >16.31</td>\n",
       "      <td id=\"T_4bbac_row3_col3\" class=\"data row3 col3\" >10.95</td>\n",
       "      <td id=\"T_4bbac_row3_col4\" class=\"data row3 col4\" >16.91</td>\n",
       "      <td id=\"T_4bbac_row3_col5\" class=\"data row3 col5\" >11.75</td>\n",
       "      <td id=\"T_4bbac_row3_col6\" class=\"data row3 col6\" >16.04</td>\n",
       "      <td id=\"T_4bbac_row3_col7\" class=\"data row3 col7\" >10.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bbac_level1_row4\" class=\"row_heading level1 row4\" >96</th>\n",
       "      <td id=\"T_4bbac_row4_col0\" class=\"data row4 col0\" >20.02</td>\n",
       "      <td id=\"T_4bbac_row4_col1\" class=\"data row4 col1\" >14.09</td>\n",
       "      <td id=\"T_4bbac_row4_col2\" class=\"data row4 col2\" >20.14</td>\n",
       "      <td id=\"T_4bbac_row4_col3\" class=\"data row4 col3\" >14.31</td>\n",
       "      <td id=\"T_4bbac_row4_col4\" class=\"data row4 col4\" >20.69</td>\n",
       "      <td id=\"T_4bbac_row4_col5\" class=\"data row4 col5\" >14.71</td>\n",
       "      <td id=\"T_4bbac_row4_col6\" class=\"data row4 col6\" >20.07</td>\n",
       "      <td id=\"T_4bbac_row4_col7\" class=\"data row4 col7\" >14.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bbac_level1_row5\" class=\"row_heading level1 row5\" >168</th>\n",
       "      <td id=\"T_4bbac_row5_col0\" class=\"data row5 col0\" >20.62</td>\n",
       "      <td id=\"T_4bbac_row5_col1\" class=\"data row5 col1\" >14.66</td>\n",
       "      <td id=\"T_4bbac_row5_col2\" class=\"data row5 col2\" >21.95</td>\n",
       "      <td id=\"T_4bbac_row5_col3\" class=\"data row5 col3\" >15.56</td>\n",
       "      <td id=\"T_4bbac_row5_col4\" class=\"data row5 col4\" >21.21</td>\n",
       "      <td id=\"T_4bbac_row5_col5\" class=\"data row5 col5\" >15.20</td>\n",
       "      <td id=\"T_4bbac_row5_col6\" class=\"data row5 col6\" >20.75</td>\n",
       "      <td id=\"T_4bbac_row5_col7\" class=\"data row5 col7\" >14.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bbac_level0_row6\" class=\"row_heading level0 row6\" rowspan=\"3\">Spain</th>\n",
       "      <th id=\"T_4bbac_level1_row6\" class=\"row_heading level1 row6\" >24</th>\n",
       "      <td id=\"T_4bbac_row6_col0\" class=\"data row6 col0\" >9.87</td>\n",
       "      <td id=\"T_4bbac_row6_col1\" class=\"data row6 col1\" >6.07</td>\n",
       "      <td id=\"T_4bbac_row6_col2\" class=\"data row6 col2\" >13.55</td>\n",
       "      <td id=\"T_4bbac_row6_col3\" class=\"data row6 col3\" >8.41</td>\n",
       "      <td id=\"T_4bbac_row6_col4\" class=\"data row6 col4\" >10.68</td>\n",
       "      <td id=\"T_4bbac_row6_col5\" class=\"data row6 col5\" >6.92</td>\n",
       "      <td id=\"T_4bbac_row6_col6\" class=\"data row6 col6\" >10.27</td>\n",
       "      <td id=\"T_4bbac_row6_col7\" class=\"data row6 col7\" >6.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bbac_level1_row7\" class=\"row_heading level1 row7\" >96</th>\n",
       "      <td id=\"T_4bbac_row7_col0\" class=\"data row7 col0\" >13.49</td>\n",
       "      <td id=\"T_4bbac_row7_col1\" class=\"data row7 col1\" >8.79</td>\n",
       "      <td id=\"T_4bbac_row7_col2\" class=\"data row7 col2\" >17.61</td>\n",
       "      <td id=\"T_4bbac_row7_col3\" class=\"data row7 col3\" >11.43</td>\n",
       "      <td id=\"T_4bbac_row7_col4\" class=\"data row7 col4\" >14.37</td>\n",
       "      <td id=\"T_4bbac_row7_col5\" class=\"data row7 col5\" >9.65</td>\n",
       "      <td id=\"T_4bbac_row7_col6\" class=\"data row7 col6\" >13.88</td>\n",
       "      <td id=\"T_4bbac_row7_col7\" class=\"data row7 col7\" >9.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bbac_level1_row8\" class=\"row_heading level1 row8\" >168</th>\n",
       "      <td id=\"T_4bbac_row8_col0\" class=\"data row8 col0\" >14.21</td>\n",
       "      <td id=\"T_4bbac_row8_col1\" class=\"data row8 col1\" >9.48</td>\n",
       "      <td id=\"T_4bbac_row8_col2\" class=\"data row8 col2\" >18.14</td>\n",
       "      <td id=\"T_4bbac_row8_col3\" class=\"data row8 col3\" >12.12</td>\n",
       "      <td id=\"T_4bbac_row8_col4\" class=\"data row8 col4\" >15.14</td>\n",
       "      <td id=\"T_4bbac_row8_col5\" class=\"data row8 col5\" >10.32</td>\n",
       "      <td id=\"T_4bbac_row8_col6\" class=\"data row8 col6\" >14.63</td>\n",
       "      <td id=\"T_4bbac_row8_col7\" class=\"data row8 col7\" >9.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bbac_level0_row9\" class=\"row_heading level0 row9\" rowspan=\"3\">France</th>\n",
       "      <th id=\"T_4bbac_level1_row9\" class=\"row_heading level1 row9\" >24</th>\n",
       "      <td id=\"T_4bbac_row9_col0\" class=\"data row9 col0\" >9.97</td>\n",
       "      <td id=\"T_4bbac_row9_col1\" class=\"data row9 col1\" >5.66</td>\n",
       "      <td id=\"T_4bbac_row9_col2\" class=\"data row9 col2\" >10.48</td>\n",
       "      <td id=\"T_4bbac_row9_col3\" class=\"data row9 col3\" >6.40</td>\n",
       "      <td id=\"T_4bbac_row9_col4\" class=\"data row9 col4\" >10.49</td>\n",
       "      <td id=\"T_4bbac_row9_col5\" class=\"data row9 col5\" >6.11</td>\n",
       "      <td id=\"T_4bbac_row9_col6\" class=\"data row9 col6\" >10.42</td>\n",
       "      <td id=\"T_4bbac_row9_col7\" class=\"data row9 col7\" >5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bbac_level1_row10\" class=\"row_heading level1 row10\" >96</th>\n",
       "      <td id=\"T_4bbac_row10_col0\" class=\"data row10 col0\" >13.63</td>\n",
       "      <td id=\"T_4bbac_row10_col1\" class=\"data row10 col1\" >8.20</td>\n",
       "      <td id=\"T_4bbac_row10_col2\" class=\"data row10 col2\" >13.92</td>\n",
       "      <td id=\"T_4bbac_row10_col3\" class=\"data row10 col3\" >8.73</td>\n",
       "      <td id=\"T_4bbac_row10_col4\" class=\"data row10 col4\" >13.83</td>\n",
       "      <td id=\"T_4bbac_row10_col5\" class=\"data row10 col5\" >8.49</td>\n",
       "      <td id=\"T_4bbac_row10_col6\" class=\"data row10 col6\" >14.15</td>\n",
       "      <td id=\"T_4bbac_row10_col7\" class=\"data row10 col7\" >8.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bbac_level1_row11\" class=\"row_heading level1 row11\" >168</th>\n",
       "      <td id=\"T_4bbac_row11_col0\" class=\"data row11 col0\" >14.26</td>\n",
       "      <td id=\"T_4bbac_row11_col1\" class=\"data row11 col1\" >8.86</td>\n",
       "      <td id=\"T_4bbac_row11_col2\" class=\"data row11 col2\" >14.65</td>\n",
       "      <td id=\"T_4bbac_row11_col3\" class=\"data row11 col3\" >9.29</td>\n",
       "      <td id=\"T_4bbac_row11_col4\" class=\"data row11 col4\" >14.13</td>\n",
       "      <td id=\"T_4bbac_row11_col5\" class=\"data row11 col5\" >8.89</td>\n",
       "      <td id=\"T_4bbac_row11_col6\" class=\"data row11 col6\" >14.83</td>\n",
       "      <td id=\"T_4bbac_row11_col7\" class=\"data row11 col7\" >9.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bbac_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"3\">Italy</th>\n",
       "      <th id=\"T_4bbac_level1_row12\" class=\"row_heading level1 row12\" >24</th>\n",
       "      <td id=\"T_4bbac_row12_col0\" class=\"data row12 col0\" >9.95</td>\n",
       "      <td id=\"T_4bbac_row12_col1\" class=\"data row12 col1\" >5.82</td>\n",
       "      <td id=\"T_4bbac_row12_col2\" class=\"data row12 col2\" >10.42</td>\n",
       "      <td id=\"T_4bbac_row12_col3\" class=\"data row12 col3\" >6.58</td>\n",
       "      <td id=\"T_4bbac_row12_col4\" class=\"data row12 col4\" >10.33</td>\n",
       "      <td id=\"T_4bbac_row12_col5\" class=\"data row12 col5\" >6.20</td>\n",
       "      <td id=\"T_4bbac_row12_col6\" class=\"data row12 col6\" >10.12</td>\n",
       "      <td id=\"T_4bbac_row12_col7\" class=\"data row12 col7\" >6.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bbac_level1_row13\" class=\"row_heading level1 row13\" >96</th>\n",
       "      <td id=\"T_4bbac_row13_col0\" class=\"data row13 col0\" >13.15</td>\n",
       "      <td id=\"T_4bbac_row13_col1\" class=\"data row13 col1\" >7.98</td>\n",
       "      <td id=\"T_4bbac_row13_col2\" class=\"data row13 col2\" >13.37</td>\n",
       "      <td id=\"T_4bbac_row13_col3\" class=\"data row13 col3\" >8.60</td>\n",
       "      <td id=\"T_4bbac_row13_col4\" class=\"data row13 col4\" >13.72</td>\n",
       "      <td id=\"T_4bbac_row13_col5\" class=\"data row13 col5\" >8.60</td>\n",
       "      <td id=\"T_4bbac_row13_col6\" class=\"data row13 col6\" >13.53</td>\n",
       "      <td id=\"T_4bbac_row13_col7\" class=\"data row13 col7\" >8.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4bbac_level1_row14\" class=\"row_heading level1 row14\" >168</th>\n",
       "      <td id=\"T_4bbac_row14_col0\" class=\"data row14 col0\" >13.75</td>\n",
       "      <td id=\"T_4bbac_row14_col1\" class=\"data row14 col1\" >8.58</td>\n",
       "      <td id=\"T_4bbac_row14_col2\" class=\"data row14 col2\" >13.75</td>\n",
       "      <td id=\"T_4bbac_row14_col3\" class=\"data row14 col3\" >8.97</td>\n",
       "      <td id=\"T_4bbac_row14_col4\" class=\"data row14 col4\" >14.08</td>\n",
       "      <td id=\"T_4bbac_row14_col5\" class=\"data row14 col5\" >9.02</td>\n",
       "      <td id=\"T_4bbac_row14_col6\" class=\"data row14 col6\" >13.94</td>\n",
       "      <td id=\"T_4bbac_row14_col7\" class=\"data row14 col7\" >8.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f24fb1253d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read data\n",
    "no_revin = read_results_csv('patchtst/patchtst_no_revin.csv', \n",
    "                           columns_to_extract = ('-RevIN', ['RMSE', 'MAE'])\n",
    "                           )\n",
    "patchtst_mix_features = read_results_csv('patchtst/patchtst_channel_mixing_MIX_FEATURES.csv',\n",
    "                                        columns_to_extract = ('CM', ['RMSE', 'MAE'])\n",
    "                                        )\n",
    "\n",
    "patchtst_mix_corr = read_results_csv('patchtst/channel_mixing_new.csv',\n",
    "                                        columns_to_extract = ('CM', ['RMSE', 'MAE'])\n",
    "                                        )\n",
    "no_patch = read_results_csv('patchtst/patchtst_no_patching.csv', \n",
    "                           columns_to_extract = ('-P', ['RMSE', 'MAE'])\n",
    "                           )\n",
    "\n",
    "all_df = pd.concat([no_revin, patchtst_mix_features, no_patch], axis=1)\n",
    "\n",
    "# Change country codes to country names\n",
    "all_df.index = pd.MultiIndex.from_tuples(\n",
    "    [(map_country_name(x[0]), x[1]) for x in all_df.index],\n",
    "    names=level_names\n",
    ")\n",
    "\n",
    "all_df = pd.concat([result_df_unscaled, all_df], axis=1)\n",
    "\n",
    "# Scale\n",
    "all_df_scale = all_df * scale\n",
    "\n",
    "# Highlight maximum values in bold\n",
    "styled_df = style_dataframe(all_df_scale, 'max', decimal_places=2)\n",
    "styled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th colspan=\"2\" halign=\"left\">w/o RevIN</th>\n",
       "      <th colspan=\"2\" halign=\"left\">CM</th>\n",
       "      <th colspan=\"2\" halign=\"left\">w/o Patching</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Metrics</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Pred_len</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Germany</th>\n",
       "      <th>24</th>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>0.0153</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0037</td>\n",
       "      <td>0.0039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.0045</td>\n",
       "      <td>0.0024</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0102</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.0035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">United Kingdom</th>\n",
       "      <th>24</th>\n",
       "      <td>0.0067</td>\n",
       "      <td>0.0069</td>\n",
       "      <td>0.0126</td>\n",
       "      <td>0.0148</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>0.0030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>0.0133</td>\n",
       "      <td>0.0091</td>\n",
       "      <td>0.0059</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Spain</th>\n",
       "      <th>24</th>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.0234</td>\n",
       "      <td>0.0081</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>0.0041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.0412</td>\n",
       "      <td>0.0263</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>0.0043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>0.0393</td>\n",
       "      <td>0.0264</td>\n",
       "      <td>0.0092</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.0033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">France</th>\n",
       "      <th>24</th>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>0.0033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0053</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>0.0039</td>\n",
       "      <td>0.0043</td>\n",
       "      <td>-0.0013</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Italy</th>\n",
       "      <th>24</th>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0076</td>\n",
       "      <td>0.0038</td>\n",
       "      <td>0.0038</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.0038</td>\n",
       "      <td>0.0034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Model                   w/o RevIN              CM         w/o Patching        \n",
       "Metrics                      RMSE     MAE    RMSE     MAE         RMSE     MAE\n",
       "Country        Pred_len                                                       \n",
       "Germany        24          0.0034  0.0041  0.0153  0.0150       0.0037  0.0039\n",
       "               96          0.0045  0.0024  0.0093  0.0102       0.0022  0.0035\n",
       "               168         0.0048  0.0019  0.0074  0.0074       0.0012  0.0011\n",
       "United Kingdom 24          0.0067  0.0069  0.0126  0.0148       0.0039  0.0030\n",
       "               96          0.0012  0.0021  0.0067  0.0062       0.0006  0.0006\n",
       "               168         0.0133  0.0091  0.0059  0.0055       0.0013  0.0004\n",
       "Spain          24          0.0368  0.0234  0.0081  0.0085       0.0041  0.0041\n",
       "               96          0.0412  0.0263  0.0088  0.0086       0.0039  0.0043\n",
       "               168         0.0393  0.0264  0.0092  0.0085       0.0042  0.0033\n",
       "France         24          0.0051  0.0074  0.0052  0.0044       0.0045  0.0033\n",
       "               96          0.0029  0.0053  0.0020  0.0029       0.0052  0.0037\n",
       "               168         0.0039  0.0043 -0.0013  0.0003       0.0057  0.0025\n",
       "Italy          24          0.0048  0.0076  0.0038  0.0038       0.0018  0.0025\n",
       "               96          0.0022  0.0062  0.0057  0.0062       0.0038  0.0034\n",
       "               168         0.0000  0.0039  0.0033  0.0044       0.0019  0.0019"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate difference\n",
    "models = ['-RevIN', 'CM', '-P']\n",
    "metrics = ['RMSE', 'MAE']\n",
    "\n",
    "results = {}\n",
    "for model in models:\n",
    "    for metric in metrics:\n",
    "        results[(model, metric)] = all_df.loc[:, (model, metric)] - all_df.loc[:, ('PatchTST', metric)]\n",
    "\n",
    "diff_df = pd.DataFrame(results)\n",
    "level_names = all_df.index.names\n",
    "diff_df.columns = pd.MultiIndex.from_tuples(diff_df.columns, names=all_df.columns.names)\n",
    "\n",
    "# Rename for plot\n",
    "rename_map = {'-RevIN': 'w/o RevIN', 'CM': 'CM', '-P': 'w/o Patching'}\n",
    "new_columns = diff_df.columns.to_frame(index=False)\n",
    "new_columns['Model'] = new_columns['Model'].replace(rename_map)\n",
    "diff_df.columns = pd.MultiIndex.from_frame(new_columns)\n",
    "diff_df.round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results_models_multiple_countries(diff_df, \n",
    "                                       function=\"bars\", \n",
    "                                       eval_metric='MAE',\n",
    "                                       palette=\"Paired\",\n",
    "                                       plot_max_markers=True,\n",
    "                                       decimal_places=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results_models_multiple_countries(diff_df, \n",
    "                                       function=\"bars\", \n",
    "                                       eval_metric='RMSE',\n",
    "                                       palette=\"Paired\",\n",
    "                                       plot_max_markers=True,\n",
    "                                       decimal_places=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1. W/o RevIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th colspan=\"2\" halign=\"left\">PatchTST</th>\n",
       "      <th colspan=\"2\" halign=\"left\">-RevIN</th>\n",
       "      <th>rmse_improvement</th>\n",
       "      <th>mae_improvement</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Metrics</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Pred_len</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Germany</th>\n",
       "      <th>24</th>\n",
       "      <td>14.43</td>\n",
       "      <td>9.11</td>\n",
       "      <td>14.77</td>\n",
       "      <td>9.52</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>18.61</td>\n",
       "      <td>12.82</td>\n",
       "      <td>19.06</td>\n",
       "      <td>13.06</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>19.27</td>\n",
       "      <td>13.54</td>\n",
       "      <td>19.75</td>\n",
       "      <td>13.73</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">United Kingdom</th>\n",
       "      <th>24</th>\n",
       "      <td>15.65</td>\n",
       "      <td>10.27</td>\n",
       "      <td>16.31</td>\n",
       "      <td>10.95</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>20.02</td>\n",
       "      <td>14.09</td>\n",
       "      <td>20.14</td>\n",
       "      <td>14.31</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>20.62</td>\n",
       "      <td>14.66</td>\n",
       "      <td>21.95</td>\n",
       "      <td>15.56</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Spain</th>\n",
       "      <th>24</th>\n",
       "      <td>9.87</td>\n",
       "      <td>6.07</td>\n",
       "      <td>13.55</td>\n",
       "      <td>8.41</td>\n",
       "      <td>3.68</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.49</td>\n",
       "      <td>8.79</td>\n",
       "      <td>17.61</td>\n",
       "      <td>11.43</td>\n",
       "      <td>4.12</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>14.21</td>\n",
       "      <td>9.48</td>\n",
       "      <td>18.14</td>\n",
       "      <td>12.12</td>\n",
       "      <td>3.93</td>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">France</th>\n",
       "      <th>24</th>\n",
       "      <td>9.97</td>\n",
       "      <td>5.66</td>\n",
       "      <td>10.48</td>\n",
       "      <td>6.40</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.63</td>\n",
       "      <td>8.20</td>\n",
       "      <td>13.92</td>\n",
       "      <td>8.73</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>14.26</td>\n",
       "      <td>8.86</td>\n",
       "      <td>14.65</td>\n",
       "      <td>9.29</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Italy</th>\n",
       "      <th>24</th>\n",
       "      <td>9.95</td>\n",
       "      <td>5.82</td>\n",
       "      <td>10.42</td>\n",
       "      <td>6.58</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.15</td>\n",
       "      <td>7.98</td>\n",
       "      <td>13.37</td>\n",
       "      <td>8.60</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>13.75</td>\n",
       "      <td>8.58</td>\n",
       "      <td>13.75</td>\n",
       "      <td>8.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Model                   PatchTST        -RevIN        rmse_improvement  \\\n",
       "Metrics                     RMSE    MAE   RMSE    MAE                    \n",
       "Country        Pred_len                                                  \n",
       "Germany        24          14.43   9.11  14.77   9.52             0.34   \n",
       "               96          18.61  12.82  19.06  13.06             0.45   \n",
       "               168         19.27  13.54  19.75  13.73             0.48   \n",
       "United Kingdom 24          15.65  10.27  16.31  10.95             0.67   \n",
       "               96          20.02  14.09  20.14  14.31             0.12   \n",
       "               168         20.62  14.66  21.95  15.56             1.33   \n",
       "Spain          24           9.87   6.07  13.55   8.41             3.68   \n",
       "               96          13.49   8.79  17.61  11.43             4.12   \n",
       "               168         14.21   9.48  18.14  12.12             3.93   \n",
       "France         24           9.97   5.66  10.48   6.40             0.51   \n",
       "               96          13.63   8.20  13.92   8.73             0.29   \n",
       "               168         14.26   8.86  14.65   9.29             0.39   \n",
       "Italy          24           9.95   5.82  10.42   6.58             0.48   \n",
       "               96          13.15   7.98  13.37   8.60             0.22   \n",
       "               168         13.75   8.58  13.75   8.97             0.00   \n",
       "\n",
       "Model                   mae_improvement  \n",
       "Metrics                                  \n",
       "Country        Pred_len                  \n",
       "Germany        24                  0.41  \n",
       "               96                  0.24  \n",
       "               168                 0.19  \n",
       "United Kingdom 24                  0.69  \n",
       "               96                  0.21  \n",
       "               168                 0.91  \n",
       "Spain          24                  2.34  \n",
       "               96                  2.63  \n",
       "               168                 2.64  \n",
       "France         24                  0.74  \n",
       "               96                  0.53  \n",
       "               168                 0.43  \n",
       "Italy          24                  0.76  \n",
       "               96                  0.62  \n",
       "               168                 0.39  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_revin.index = pd.MultiIndex.from_tuples(\n",
    "    [(map_country_name(x[0]), x[1]) for x in no_revin.index],\n",
    "    names=level_names\n",
    ")\n",
    "\n",
    "revin = pd.concat([result_df_unscaled, no_revin], axis=1)\n",
    "\n",
    "# Scale\n",
    "revin = revin * scale\n",
    "\n",
    "revin['rmse_improvement'] = (revin.loc[:, ('-RevIN', 'RMSE')] - (revin.loc[:, ('PatchTST', 'RMSE')]))\n",
    "revin['mae_improvement'] = (revin.loc[:, ('-RevIN', 'MAE')] - revin.loc[:, ('PatchTST', 'MAE')])\n",
    "revin.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "decimal_places = 2\n",
    "format_string = f\"{{:.{decimal_places}f}}\"\n",
    "styled_df = revin.style.format(format_string)\n",
    "styled_df.to_html(os.path.join(path, 'no_revin_10_power_2.html'), index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average reduction of MAE: -11.2 %.\n",
      "Average reduction of RMSE: -8.7 %.\n"
     ]
    }
   ],
   "source": [
    "revin = calculate_improvement(revin, \n",
    "                      base_mae_model='-RevIN', \n",
    "                      model_to_compare_mae='PatchTST',\n",
    "                      grouped_by_models=False).round(2)\n",
    "print(f\"Average reduction of MAE: {(revin.loc[revin['mae_improvement']!=0]['mae_improvement'].mean()).round(1)} %.\")\n",
    "print(f\"Average reduction of RMSE: {(revin.loc[revin['rmse_improvement']!=0]['rmse_improvement'].mean()).round(1)} %.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th colspan=\"2\" halign=\"left\">PatchTST</th>\n",
       "      <th colspan=\"2\" halign=\"left\">-RevIN</th>\n",
       "      <th>rmse_improvement</th>\n",
       "      <th>mae_improvement</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metrics</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>12.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>-8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>17.4</td>\n",
       "      <td>11.8</td>\n",
       "      <td>17.9</td>\n",
       "      <td>12.1</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>-2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>12.3</td>\n",
       "      <td>7.5</td>\n",
       "      <td>12.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>-8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>12.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>16.4</td>\n",
       "      <td>10.7</td>\n",
       "      <td>-31.9</td>\n",
       "      <td>-32.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>18.8</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>13.6</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>-4.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Model          PatchTST       -RevIN       rmse_improvement mae_improvement\n",
       "Metrics            RMSE   MAE   RMSE   MAE                                 \n",
       "Country                                                                    \n",
       "France             12.6   7.6   13.0   8.1             -3.3            -8.1\n",
       "Germany            17.4  11.8   17.9  12.1             -2.4            -2.6\n",
       "Italy              12.3   7.5   12.5   8.0             -2.2            -8.4\n",
       "Spain              12.5   8.1   16.4  10.7            -31.9           -32.1\n",
       "United Kingdom     18.8  13.0   19.5  13.6             -3.8            -4.8"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revin.groupby('Country').mean().round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2. W/o channel independence (channel-mixing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th colspan=\"2\" halign=\"left\">PatchTST</th>\n",
       "      <th colspan=\"2\" halign=\"left\">CM</th>\n",
       "      <th>rmse_improvement</th>\n",
       "      <th>mae_improvement</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Metrics</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Pred_len</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Germany</th>\n",
       "      <th>24</th>\n",
       "      <td>14.43</td>\n",
       "      <td>9.11</td>\n",
       "      <td>15.96</td>\n",
       "      <td>10.60</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>18.61</td>\n",
       "      <td>12.82</td>\n",
       "      <td>19.53</td>\n",
       "      <td>13.84</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>19.27</td>\n",
       "      <td>13.54</td>\n",
       "      <td>20.01</td>\n",
       "      <td>14.28</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">United Kingdom</th>\n",
       "      <th>24</th>\n",
       "      <td>15.65</td>\n",
       "      <td>10.27</td>\n",
       "      <td>16.91</td>\n",
       "      <td>11.75</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>20.02</td>\n",
       "      <td>14.09</td>\n",
       "      <td>20.69</td>\n",
       "      <td>14.71</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>20.62</td>\n",
       "      <td>14.66</td>\n",
       "      <td>21.21</td>\n",
       "      <td>15.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Spain</th>\n",
       "      <th>24</th>\n",
       "      <td>9.87</td>\n",
       "      <td>6.07</td>\n",
       "      <td>10.68</td>\n",
       "      <td>6.92</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.49</td>\n",
       "      <td>8.79</td>\n",
       "      <td>14.37</td>\n",
       "      <td>9.65</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>14.21</td>\n",
       "      <td>9.48</td>\n",
       "      <td>15.14</td>\n",
       "      <td>10.32</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">France</th>\n",
       "      <th>24</th>\n",
       "      <td>9.97</td>\n",
       "      <td>5.66</td>\n",
       "      <td>10.49</td>\n",
       "      <td>6.11</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.63</td>\n",
       "      <td>8.20</td>\n",
       "      <td>13.83</td>\n",
       "      <td>8.49</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>14.26</td>\n",
       "      <td>8.86</td>\n",
       "      <td>14.13</td>\n",
       "      <td>8.89</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Italy</th>\n",
       "      <th>24</th>\n",
       "      <td>9.95</td>\n",
       "      <td>5.82</td>\n",
       "      <td>10.33</td>\n",
       "      <td>6.20</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.15</td>\n",
       "      <td>7.98</td>\n",
       "      <td>13.72</td>\n",
       "      <td>8.60</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>13.75</td>\n",
       "      <td>8.58</td>\n",
       "      <td>14.08</td>\n",
       "      <td>9.02</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Model                   PatchTST            CM        rmse_improvement  \\\n",
       "Metrics                     RMSE    MAE   RMSE    MAE                    \n",
       "Country        Pred_len                                                  \n",
       "Germany        24          14.43   9.11  15.96  10.60             1.53   \n",
       "               96          18.61  12.82  19.53  13.84             0.93   \n",
       "               168         19.27  13.54  20.01  14.28             0.74   \n",
       "United Kingdom 24          15.65  10.27  16.91  11.75             1.26   \n",
       "               96          20.02  14.09  20.69  14.71             0.67   \n",
       "               168         20.62  14.66  21.21  15.20             0.59   \n",
       "Spain          24           9.87   6.07  10.68   6.92             0.81   \n",
       "               96          13.49   8.79  14.37   9.65             0.88   \n",
       "               168         14.21   9.48  15.14  10.32             0.92   \n",
       "France         24           9.97   5.66  10.49   6.11             0.52   \n",
       "               96          13.63   8.20  13.83   8.49             0.20   \n",
       "               168         14.26   8.86  14.13   8.89            -0.13   \n",
       "Italy          24           9.95   5.82  10.33   6.20             0.38   \n",
       "               96          13.15   7.98  13.72   8.60             0.57   \n",
       "               168         13.75   8.58  14.08   9.02             0.33   \n",
       "\n",
       "Model                   mae_improvement  \n",
       "Metrics                                  \n",
       "Country        Pred_len                  \n",
       "Germany        24                  1.50  \n",
       "               96                  1.02  \n",
       "               168                 0.74  \n",
       "United Kingdom 24                  1.48  \n",
       "               96                  0.62  \n",
       "               168                 0.55  \n",
       "Spain          24                  0.85  \n",
       "               96                  0.86  \n",
       "               168                 0.85  \n",
       "France         24                  0.44  \n",
       "               96                  0.29  \n",
       "               168                 0.03  \n",
       "Italy          24                  0.38  \n",
       "               96                  0.62  \n",
       "               168                 0.44  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patchtst_mix_features.index = pd.MultiIndex.from_tuples(\n",
    "    [(map_country_name(x[0]), x[1]) for x in patchtst_mix_features.index],\n",
    "    names=level_names\n",
    ")\n",
    "\n",
    "patchtst_mix_features = pd.concat([result_df_unscaled, patchtst_mix_features], axis=1)\n",
    "\n",
    "# Scale\n",
    "patchtst_mix_features = patchtst_mix_features * scale\n",
    "\n",
    "patchtst_mix_features['rmse_improvement'] = (patchtst_mix_features.loc[:, ('CM', 'RMSE')] - patchtst_mix_features.loc[:, ('PatchTST', 'RMSE')]) \n",
    "patchtst_mix_features['mae_improvement'] = (patchtst_mix_features.loc[:, ('CM', 'MAE')] - patchtst_mix_features.loc[:, ('PatchTST', 'MAE')]) \n",
    "patchtst_mix_features.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "decimal_places = 2\n",
    "format_string = f\"{{:.{decimal_places}f}}\"\n",
    "styled_df = patchtst_mix_features.style.format(format_string)\n",
    "styled_df.to_html(os.path.join(path, 'cm_10_power_2.html'), index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average reduction of MAE: -7.7 %.\n",
      "Average reduction of RMSE: -4.8 %.\n"
     ]
    }
   ],
   "source": [
    "patchtst_mix_features = calculate_improvement(patchtst_mix_features, \n",
    "                                              base_mae_model='CM', \n",
    "                                              model_to_compare_mae='PatchTST',\n",
    "                                              grouped_by_models=False).round(2)\n",
    "print(f\"Average reduction of MAE: {(patchtst_mix_features.loc[patchtst_mix_features['mae_improvement']!=0]['mae_improvement'].mean()).round(1)} %.\")\n",
    "print(f\"Average reduction of RMSE: {(patchtst_mix_features.loc[patchtst_mix_features['rmse_improvement']!=0]['rmse_improvement'].mean()).round(1)} %.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th colspan=\"2\" halign=\"left\">PatchTST</th>\n",
       "      <th colspan=\"2\" halign=\"left\">CM</th>\n",
       "      <th>rmse_improvement</th>\n",
       "      <th>mae_improvement</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Metrics</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Pred_len</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Germany</th>\n",
       "      <th>24</th>\n",
       "      <td>14.43</td>\n",
       "      <td>9.11</td>\n",
       "      <td>15.29</td>\n",
       "      <td>10.06</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>18.61</td>\n",
       "      <td>12.82</td>\n",
       "      <td>19.53</td>\n",
       "      <td>13.84</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>19.27</td>\n",
       "      <td>13.54</td>\n",
       "      <td>20.01</td>\n",
       "      <td>14.28</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">United Kingdom</th>\n",
       "      <th>24</th>\n",
       "      <td>15.65</td>\n",
       "      <td>10.27</td>\n",
       "      <td>16.30</td>\n",
       "      <td>10.94</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>20.02</td>\n",
       "      <td>14.09</td>\n",
       "      <td>20.69</td>\n",
       "      <td>14.71</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>20.62</td>\n",
       "      <td>14.66</td>\n",
       "      <td>21.21</td>\n",
       "      <td>15.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Spain</th>\n",
       "      <th>24</th>\n",
       "      <td>9.87</td>\n",
       "      <td>6.07</td>\n",
       "      <td>10.68</td>\n",
       "      <td>6.92</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.49</td>\n",
       "      <td>8.79</td>\n",
       "      <td>14.37</td>\n",
       "      <td>9.65</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>14.21</td>\n",
       "      <td>9.48</td>\n",
       "      <td>15.14</td>\n",
       "      <td>10.32</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">France</th>\n",
       "      <th>24</th>\n",
       "      <td>9.97</td>\n",
       "      <td>5.66</td>\n",
       "      <td>10.49</td>\n",
       "      <td>6.11</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.63</td>\n",
       "      <td>8.20</td>\n",
       "      <td>13.83</td>\n",
       "      <td>8.49</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>14.26</td>\n",
       "      <td>8.86</td>\n",
       "      <td>14.13</td>\n",
       "      <td>8.89</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Italy</th>\n",
       "      <th>24</th>\n",
       "      <td>9.95</td>\n",
       "      <td>5.82</td>\n",
       "      <td>10.33</td>\n",
       "      <td>6.20</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.15</td>\n",
       "      <td>7.98</td>\n",
       "      <td>13.72</td>\n",
       "      <td>8.60</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>13.75</td>\n",
       "      <td>8.58</td>\n",
       "      <td>14.08</td>\n",
       "      <td>9.02</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Model                   PatchTST            CM        rmse_improvement  \\\n",
       "Metrics                     RMSE    MAE   RMSE    MAE                    \n",
       "Country        Pred_len                                                  \n",
       "Germany        24          14.43   9.11  15.29  10.06             0.86   \n",
       "               96          18.61  12.82  19.53  13.84             0.93   \n",
       "               168         19.27  13.54  20.01  14.28             0.74   \n",
       "United Kingdom 24          15.65  10.27  16.30  10.94             0.65   \n",
       "               96          20.02  14.09  20.69  14.71             0.67   \n",
       "               168         20.62  14.66  21.21  15.20             0.59   \n",
       "Spain          24           9.87   6.07  10.68   6.92             0.81   \n",
       "               96          13.49   8.79  14.37   9.65             0.88   \n",
       "               168         14.21   9.48  15.14  10.32             0.92   \n",
       "France         24           9.97   5.66  10.49   6.11             0.52   \n",
       "               96          13.63   8.20  13.83   8.49             0.20   \n",
       "               168         14.26   8.86  14.13   8.89            -0.13   \n",
       "Italy          24           9.95   5.82  10.33   6.20             0.38   \n",
       "               96          13.15   7.98  13.72   8.60             0.57   \n",
       "               168         13.75   8.58  14.08   9.02             0.33   \n",
       "\n",
       "Model                   mae_improvement  \n",
       "Metrics                                  \n",
       "Country        Pred_len                  \n",
       "Germany        24                  0.95  \n",
       "               96                  1.02  \n",
       "               168                 0.74  \n",
       "United Kingdom 24                  0.68  \n",
       "               96                  0.62  \n",
       "               168                 0.55  \n",
       "Spain          24                  0.85  \n",
       "               96                  0.86  \n",
       "               168                 0.85  \n",
       "France         24                  0.44  \n",
       "               96                  0.29  \n",
       "               168                 0.03  \n",
       "Italy          24                  0.38  \n",
       "               96                  0.62  \n",
       "               168                 0.44  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patchtst_mix_corr.index = pd.MultiIndex.from_tuples(\n",
    "    [(map_country_name(x[0]), x[1]) for x in patchtst_mix_corr.index],\n",
    "    names=level_names\n",
    ")\n",
    "\n",
    "patchtst_mix_corr = pd.concat([result_df_unscaled, patchtst_mix_corr], axis=1)\n",
    "\n",
    "# Scale\n",
    "patchtst_mix_corr = patchtst_mix_corr * scale\n",
    "\n",
    "patchtst_mix_corr['rmse_improvement'] = (patchtst_mix_corr.loc[:, ('CM', 'RMSE')] - patchtst_mix_corr.loc[:, ('PatchTST', 'RMSE')]) \n",
    "patchtst_mix_corr['mae_improvement'] = (patchtst_mix_corr.loc[:, ('CM', 'MAE')] - patchtst_mix_corr.loc[:, ('PatchTST', 'MAE')]) \n",
    "patchtst_mix_corr.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3. W/o patching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th colspan=\"2\" halign=\"left\">PatchTST</th>\n",
       "      <th colspan=\"2\" halign=\"left\">-P</th>\n",
       "      <th>rmse_improvement</th>\n",
       "      <th>mae_improvement</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Metrics</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>Pred_len</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Germany</th>\n",
       "      <th>24</th>\n",
       "      <td>14.43</td>\n",
       "      <td>9.11</td>\n",
       "      <td>14.80</td>\n",
       "      <td>9.50</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>18.61</td>\n",
       "      <td>12.82</td>\n",
       "      <td>18.82</td>\n",
       "      <td>13.17</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>19.27</td>\n",
       "      <td>13.54</td>\n",
       "      <td>19.39</td>\n",
       "      <td>13.65</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">United Kingdom</th>\n",
       "      <th>24</th>\n",
       "      <td>15.65</td>\n",
       "      <td>10.27</td>\n",
       "      <td>16.04</td>\n",
       "      <td>10.57</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>20.02</td>\n",
       "      <td>14.09</td>\n",
       "      <td>20.07</td>\n",
       "      <td>14.15</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>20.62</td>\n",
       "      <td>14.66</td>\n",
       "      <td>20.75</td>\n",
       "      <td>14.70</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Spain</th>\n",
       "      <th>24</th>\n",
       "      <td>9.87</td>\n",
       "      <td>6.07</td>\n",
       "      <td>10.27</td>\n",
       "      <td>6.48</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.49</td>\n",
       "      <td>8.79</td>\n",
       "      <td>13.88</td>\n",
       "      <td>9.23</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>14.21</td>\n",
       "      <td>9.48</td>\n",
       "      <td>14.63</td>\n",
       "      <td>9.81</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">France</th>\n",
       "      <th>24</th>\n",
       "      <td>9.97</td>\n",
       "      <td>5.66</td>\n",
       "      <td>10.42</td>\n",
       "      <td>5.99</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.63</td>\n",
       "      <td>8.20</td>\n",
       "      <td>14.15</td>\n",
       "      <td>8.58</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>14.26</td>\n",
       "      <td>8.86</td>\n",
       "      <td>14.83</td>\n",
       "      <td>9.11</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Italy</th>\n",
       "      <th>24</th>\n",
       "      <td>9.95</td>\n",
       "      <td>5.82</td>\n",
       "      <td>10.12</td>\n",
       "      <td>6.07</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>13.15</td>\n",
       "      <td>7.98</td>\n",
       "      <td>13.53</td>\n",
       "      <td>8.31</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>13.75</td>\n",
       "      <td>8.58</td>\n",
       "      <td>13.94</td>\n",
       "      <td>8.77</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Model                   PatchTST            -P        rmse_improvement  \\\n",
       "Metrics                     RMSE    MAE   RMSE    MAE                    \n",
       "Country        Pred_len                                                  \n",
       "Germany        24          14.43   9.11  14.80   9.50             0.37   \n",
       "               96          18.61  12.82  18.82  13.17             0.22   \n",
       "               168         19.27  13.54  19.39  13.65             0.12   \n",
       "United Kingdom 24          15.65  10.27  16.04  10.57             0.39   \n",
       "               96          20.02  14.09  20.07  14.15             0.06   \n",
       "               168         20.62  14.66  20.75  14.70             0.13   \n",
       "Spain          24           9.87   6.07  10.27   6.48             0.41   \n",
       "               96          13.49   8.79  13.88   9.23             0.39   \n",
       "               168         14.21   9.48  14.63   9.81             0.42   \n",
       "France         24           9.97   5.66  10.42   5.99             0.45   \n",
       "               96          13.63   8.20  14.15   8.58             0.52   \n",
       "               168         14.26   8.86  14.83   9.11             0.57   \n",
       "Italy          24           9.95   5.82  10.12   6.07             0.18   \n",
       "               96          13.15   7.98  13.53   8.31             0.38   \n",
       "               168         13.75   8.58  13.94   8.77             0.19   \n",
       "\n",
       "Model                   mae_improvement  \n",
       "Metrics                                  \n",
       "Country        Pred_len                  \n",
       "Germany        24                  0.39  \n",
       "               96                  0.35  \n",
       "               168                 0.11  \n",
       "United Kingdom 24                  0.30  \n",
       "               96                  0.06  \n",
       "               168                 0.04  \n",
       "Spain          24                  0.41  \n",
       "               96                  0.43  \n",
       "               168                 0.33  \n",
       "France         24                  0.33  \n",
       "               96                  0.37  \n",
       "               168                 0.25  \n",
       "Italy          24                  0.25  \n",
       "               96                  0.34  \n",
       "               168                 0.19  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_patch.index = pd.MultiIndex.from_tuples(\n",
    "    [(map_country_name(x[0]), x[1]) for x in no_patch.index],\n",
    "    names=level_names\n",
    ")\n",
    "\n",
    "no_patch = pd.concat([result_df_unscaled, no_patch], axis=1)\n",
    "\n",
    "# Scale\n",
    "no_patch = no_patch * scale\n",
    "\n",
    "no_patch['rmse_improvement'] = (no_patch.loc[:, ('-P', 'RMSE')] - no_patch.loc[:, ('PatchTST', 'RMSE')]) \n",
    "no_patch['mae_improvement'] = (no_patch.loc[:, ('-P', 'MAE')] - no_patch.loc[:, ('PatchTST', 'MAE')]) \n",
    "no_patch.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "decimal_places = 2\n",
    "format_string = f\"{{:.{decimal_places}f}}\"\n",
    "styled_df = no_patch.style.format(format_string)\n",
    "styled_df.to_html(os.path.join(path, 'no_patch_10_power_2.html'), index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average reduction of MAE: -3.4 %.\n",
      "Average reduction of RMSE: -2.4 %.\n"
     ]
    }
   ],
   "source": [
    "no_patch = calculate_improvement(no_patch, \n",
    "                                 base_mae_model='-P', \n",
    "                                 model_to_compare_mae='PatchTST',\n",
    "                                 grouped_by_models=False).round(2)\n",
    "print(f\"Average reduction of MAE: {(no_patch.loc[no_patch['mae_improvement']!=0]['mae_improvement'].mean()).round(1)} %.\")\n",
    "print(f\"Average reduction of RMSE: {(no_patch.loc[no_patch['rmse_improvement']!=0]['rmse_improvement'].mean()).round(1)} %.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. TimeLLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_f4fef_row0_col2, #T_f4fef_row0_col3, #T_f4fef_row1_col1, #T_f4fef_row1_col2, #T_f4fef_row2_col2, #T_f4fef_row2_col3, #T_f4fef_row3_col2, #T_f4fef_row3_col3, #T_f4fef_row4_col2, #T_f4fef_row4_col3, #T_f4fef_row5_col2, #T_f4fef_row5_col3, #T_f4fef_row6_col2, #T_f4fef_row6_col3, #T_f4fef_row7_col2, #T_f4fef_row7_col3, #T_f4fef_row8_col2, #T_f4fef_row8_col3, #T_f4fef_row9_col2, #T_f4fef_row9_col3, #T_f4fef_row10_col2, #T_f4fef_row10_col3, #T_f4fef_row11_col2, #T_f4fef_row11_col3, #T_f4fef_row12_col2, #T_f4fef_row12_col3, #T_f4fef_row13_col2, #T_f4fef_row13_col3, #T_f4fef_row14_col2, #T_f4fef_row14_col3 {\n",
       "  font-weight: bold;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_f4fef\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"index_name level0\" >Model</th>\n",
       "      <th id=\"T_f4fef_level0_col0\" class=\"col_heading level0 col0\" colspan=\"2\">TimeLLM</th>\n",
       "      <th id=\"T_f4fef_level0_col2\" class=\"col_heading level0 col2\" colspan=\"2\">PatchTST</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"index_name level1\" >Metrics</th>\n",
       "      <th id=\"T_f4fef_level1_col0\" class=\"col_heading level1 col0\" >RMSE</th>\n",
       "      <th id=\"T_f4fef_level1_col1\" class=\"col_heading level1 col1\" >MAE</th>\n",
       "      <th id=\"T_f4fef_level1_col2\" class=\"col_heading level1 col2\" >RMSE</th>\n",
       "      <th id=\"T_f4fef_level1_col3\" class=\"col_heading level1 col3\" >MAE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Country</th>\n",
       "      <th class=\"index_name level1\" >Pred_len</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f4fef_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"3\">Germany</th>\n",
       "      <th id=\"T_f4fef_level1_row0\" class=\"row_heading level1 row0\" >24</th>\n",
       "      <td id=\"T_f4fef_row0_col0\" class=\"data row0 col0\" >14.68</td>\n",
       "      <td id=\"T_f4fef_row0_col1\" class=\"data row0 col1\" >9.62</td>\n",
       "      <td id=\"T_f4fef_row0_col2\" class=\"data row0 col2\" >14.43</td>\n",
       "      <td id=\"T_f4fef_row0_col3\" class=\"data row0 col3\" >9.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4fef_level1_row1\" class=\"row_heading level1 row1\" >96</th>\n",
       "      <td id=\"T_f4fef_row1_col0\" class=\"data row1 col0\" >18.61</td>\n",
       "      <td id=\"T_f4fef_row1_col1\" class=\"data row1 col1\" >12.81</td>\n",
       "      <td id=\"T_f4fef_row1_col2\" class=\"data row1 col2\" >18.61</td>\n",
       "      <td id=\"T_f4fef_row1_col3\" class=\"data row1 col3\" >12.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4fef_level1_row2\" class=\"row_heading level1 row2\" >168</th>\n",
       "      <td id=\"T_f4fef_row2_col0\" class=\"data row2 col0\" >19.36</td>\n",
       "      <td id=\"T_f4fef_row2_col1\" class=\"data row2 col1\" >13.86</td>\n",
       "      <td id=\"T_f4fef_row2_col2\" class=\"data row2 col2\" >19.27</td>\n",
       "      <td id=\"T_f4fef_row2_col3\" class=\"data row2 col3\" >13.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4fef_level0_row3\" class=\"row_heading level0 row3\" rowspan=\"3\">United Kingdom</th>\n",
       "      <th id=\"T_f4fef_level1_row3\" class=\"row_heading level1 row3\" >24</th>\n",
       "      <td id=\"T_f4fef_row3_col0\" class=\"data row3 col0\" >16.11</td>\n",
       "      <td id=\"T_f4fef_row3_col1\" class=\"data row3 col1\" >10.74</td>\n",
       "      <td id=\"T_f4fef_row3_col2\" class=\"data row3 col2\" >15.65</td>\n",
       "      <td id=\"T_f4fef_row3_col3\" class=\"data row3 col3\" >10.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4fef_level1_row4\" class=\"row_heading level1 row4\" >96</th>\n",
       "      <td id=\"T_f4fef_row4_col0\" class=\"data row4 col0\" >20.17</td>\n",
       "      <td id=\"T_f4fef_row4_col1\" class=\"data row4 col1\" >14.28</td>\n",
       "      <td id=\"T_f4fef_row4_col2\" class=\"data row4 col2\" >20.02</td>\n",
       "      <td id=\"T_f4fef_row4_col3\" class=\"data row4 col3\" >14.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4fef_level1_row5\" class=\"row_heading level1 row5\" >168</th>\n",
       "      <td id=\"T_f4fef_row5_col0\" class=\"data row5 col0\" >20.85</td>\n",
       "      <td id=\"T_f4fef_row5_col1\" class=\"data row5 col1\" >14.94</td>\n",
       "      <td id=\"T_f4fef_row5_col2\" class=\"data row5 col2\" >20.62</td>\n",
       "      <td id=\"T_f4fef_row5_col3\" class=\"data row5 col3\" >14.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4fef_level0_row6\" class=\"row_heading level0 row6\" rowspan=\"3\">Spain</th>\n",
       "      <th id=\"T_f4fef_level1_row6\" class=\"row_heading level1 row6\" >24</th>\n",
       "      <td id=\"T_f4fef_row6_col0\" class=\"data row6 col0\" >10.44</td>\n",
       "      <td id=\"T_f4fef_row6_col1\" class=\"data row6 col1\" >6.66</td>\n",
       "      <td id=\"T_f4fef_row6_col2\" class=\"data row6 col2\" >9.87</td>\n",
       "      <td id=\"T_f4fef_row6_col3\" class=\"data row6 col3\" >6.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4fef_level1_row7\" class=\"row_heading level1 row7\" >96</th>\n",
       "      <td id=\"T_f4fef_row7_col0\" class=\"data row7 col0\" >14.17</td>\n",
       "      <td id=\"T_f4fef_row7_col1\" class=\"data row7 col1\" >9.59</td>\n",
       "      <td id=\"T_f4fef_row7_col2\" class=\"data row7 col2\" >13.49</td>\n",
       "      <td id=\"T_f4fef_row7_col3\" class=\"data row7 col3\" >8.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4fef_level1_row8\" class=\"row_heading level1 row8\" >168</th>\n",
       "      <td id=\"T_f4fef_row8_col0\" class=\"data row8 col0\" >14.80</td>\n",
       "      <td id=\"T_f4fef_row8_col1\" class=\"data row8 col1\" >10.05</td>\n",
       "      <td id=\"T_f4fef_row8_col2\" class=\"data row8 col2\" >14.21</td>\n",
       "      <td id=\"T_f4fef_row8_col3\" class=\"data row8 col3\" >9.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4fef_level0_row9\" class=\"row_heading level0 row9\" rowspan=\"3\">France</th>\n",
       "      <th id=\"T_f4fef_level1_row9\" class=\"row_heading level1 row9\" >24</th>\n",
       "      <td id=\"T_f4fef_row9_col0\" class=\"data row9 col0\" >10.19</td>\n",
       "      <td id=\"T_f4fef_row9_col1\" class=\"data row9 col1\" >5.94</td>\n",
       "      <td id=\"T_f4fef_row9_col2\" class=\"data row9 col2\" >9.97</td>\n",
       "      <td id=\"T_f4fef_row9_col3\" class=\"data row9 col3\" >5.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4fef_level1_row10\" class=\"row_heading level1 row10\" >96</th>\n",
       "      <td id=\"T_f4fef_row10_col0\" class=\"data row10 col0\" >14.02</td>\n",
       "      <td id=\"T_f4fef_row10_col1\" class=\"data row10 col1\" >8.57</td>\n",
       "      <td id=\"T_f4fef_row10_col2\" class=\"data row10 col2\" >13.63</td>\n",
       "      <td id=\"T_f4fef_row10_col3\" class=\"data row10 col3\" >8.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4fef_level1_row11\" class=\"row_heading level1 row11\" >168</th>\n",
       "      <td id=\"T_f4fef_row11_col0\" class=\"data row11 col0\" >14.58</td>\n",
       "      <td id=\"T_f4fef_row11_col1\" class=\"data row11 col1\" >9.12</td>\n",
       "      <td id=\"T_f4fef_row11_col2\" class=\"data row11 col2\" >14.26</td>\n",
       "      <td id=\"T_f4fef_row11_col3\" class=\"data row11 col3\" >8.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4fef_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"3\">Italy</th>\n",
       "      <th id=\"T_f4fef_level1_row12\" class=\"row_heading level1 row12\" >24</th>\n",
       "      <td id=\"T_f4fef_row12_col0\" class=\"data row12 col0\" >10.22</td>\n",
       "      <td id=\"T_f4fef_row12_col1\" class=\"data row12 col1\" >6.18</td>\n",
       "      <td id=\"T_f4fef_row12_col2\" class=\"data row12 col2\" >9.95</td>\n",
       "      <td id=\"T_f4fef_row12_col3\" class=\"data row12 col3\" >5.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4fef_level1_row13\" class=\"row_heading level1 row13\" >96</th>\n",
       "      <td id=\"T_f4fef_row13_col0\" class=\"data row13 col0\" >13.54</td>\n",
       "      <td id=\"T_f4fef_row13_col1\" class=\"data row13 col1\" >8.47</td>\n",
       "      <td id=\"T_f4fef_row13_col2\" class=\"data row13 col2\" >13.15</td>\n",
       "      <td id=\"T_f4fef_row13_col3\" class=\"data row13 col3\" >7.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f4fef_level1_row14\" class=\"row_heading level1 row14\" >168</th>\n",
       "      <td id=\"T_f4fef_row14_col0\" class=\"data row14 col0\" >13.97</td>\n",
       "      <td id=\"T_f4fef_row14_col1\" class=\"data row14 col1\" >8.98</td>\n",
       "      <td id=\"T_f4fef_row14_col2\" class=\"data row14 col2\" >13.75</td>\n",
       "      <td id=\"T_f4fef_row14_col3\" class=\"data row14 col3\" >8.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f24f884a410>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timellm_df = read_results_csv('timellm/timellm.csv', \n",
    "                              columns_to_extract = ('TimeLLM', ['RMSE', 'MAE'])\n",
    "                              )\n",
    "\n",
    "#all_df = pd.concat([timellm_df, timellm_df_336, patchtst_64_df, patchtst_df], axis=1)\n",
    "\n",
    "# Change country codes to country names\n",
    "timellm_df.index = pd.MultiIndex.from_tuples(\n",
    "    [(map_country_name(x[0]), x[1]) for x in timellm_df.index],\n",
    "    names=level_names\n",
    ")\n",
    "\n",
    "timellm_df_scale = timellm_df * scale\n",
    "\n",
    "df_with_timellm = pd.concat([timellm_df_scale, result_df], axis=1)\n",
    "styled_df = style_dataframe(df_with_timellm, decimal_places=2)\n",
    "styled_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c9f22_row0_col2, #T_c9f22_row0_col7, #T_c9f22_row1_col0, #T_c9f22_row1_col3, #T_c9f22_row2_col0, #T_c9f22_row2_col1, #T_c9f22_row3_col1, #T_c9f22_row3_col2, #T_c9f22_row4_col0, #T_c9f22_row4_col1, #T_c9f22_row5_col0, #T_c9f22_row5_col1, #T_c9f22_row6_col1, #T_c9f22_row6_col2, #T_c9f22_row7_col0, #T_c9f22_row7_col1, #T_c9f22_row8_col0, #T_c9f22_row8_col1, #T_c9f22_row9_col1, #T_c9f22_row9_col2, #T_c9f22_row10_col1, #T_c9f22_row10_col2, #T_c9f22_row11_col0, #T_c9f22_row11_col1, #T_c9f22_row12_col1, #T_c9f22_row12_col2, #T_c9f22_row13_col1, #T_c9f22_row13_col2, #T_c9f22_row14_col0, #T_c9f22_row14_col1 {\n",
       "  text-decoration: underline;\n",
       "}\n",
       "#T_c9f22_row0_col3, #T_c9f22_row0_col6, #T_c9f22_row1_col1, #T_c9f22_row1_col2, #T_c9f22_row2_col2, #T_c9f22_row2_col3, #T_c9f22_row3_col3, #T_c9f22_row3_col6, #T_c9f22_row4_col2, #T_c9f22_row4_col3, #T_c9f22_row5_col2, #T_c9f22_row5_col3, #T_c9f22_row6_col3, #T_c9f22_row6_col6, #T_c9f22_row7_col2, #T_c9f22_row7_col3, #T_c9f22_row8_col2, #T_c9f22_row8_col3, #T_c9f22_row9_col3, #T_c9f22_row9_col6, #T_c9f22_row10_col3, #T_c9f22_row10_col6, #T_c9f22_row11_col2, #T_c9f22_row11_col3, #T_c9f22_row12_col3, #T_c9f22_row12_col6, #T_c9f22_row13_col3, #T_c9f22_row13_col6, #T_c9f22_row14_col2, #T_c9f22_row14_col3 {\n",
       "  font-weight: bold;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c9f22\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"index_name level0\" >Model</th>\n",
       "      <th id=\"T_c9f22_level0_col0\" class=\"col_heading level0 col0\" colspan=\"2\">TimeLLM</th>\n",
       "      <th id=\"T_c9f22_level0_col2\" class=\"col_heading level0 col2\" colspan=\"2\">PatchTST</th>\n",
       "      <th id=\"T_c9f22_level0_col4\" class=\"col_heading level0 col4\" colspan=\"2\">Informer</th>\n",
       "      <th id=\"T_c9f22_level0_col6\" class=\"col_heading level0 col6\" colspan=\"2\">(S)ARIMA</th>\n",
       "      <th id=\"T_c9f22_level0_col8\" class=\"col_heading level0 col8\" colspan=\"2\">Persistence</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"index_name level1\" >Metrics</th>\n",
       "      <th id=\"T_c9f22_level1_col0\" class=\"col_heading level1 col0\" >RMSE</th>\n",
       "      <th id=\"T_c9f22_level1_col1\" class=\"col_heading level1 col1\" >MAE</th>\n",
       "      <th id=\"T_c9f22_level1_col2\" class=\"col_heading level1 col2\" >RMSE</th>\n",
       "      <th id=\"T_c9f22_level1_col3\" class=\"col_heading level1 col3\" >MAE</th>\n",
       "      <th id=\"T_c9f22_level1_col4\" class=\"col_heading level1 col4\" >RMSE</th>\n",
       "      <th id=\"T_c9f22_level1_col5\" class=\"col_heading level1 col5\" >MAE</th>\n",
       "      <th id=\"T_c9f22_level1_col6\" class=\"col_heading level1 col6\" >RMSE</th>\n",
       "      <th id=\"T_c9f22_level1_col7\" class=\"col_heading level1 col7\" >MAE</th>\n",
       "      <th id=\"T_c9f22_level1_col8\" class=\"col_heading level1 col8\" >RMSE</th>\n",
       "      <th id=\"T_c9f22_level1_col9\" class=\"col_heading level1 col9\" >MAE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Country</th>\n",
       "      <th class=\"index_name level1\" >Pred_len</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c9f22_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"3\">Germany</th>\n",
       "      <th id=\"T_c9f22_level1_row0\" class=\"row_heading level1 row0\" >24</th>\n",
       "      <td id=\"T_c9f22_row0_col0\" class=\"data row0 col0\" >14.68</td>\n",
       "      <td id=\"T_c9f22_row0_col1\" class=\"data row0 col1\" >9.62</td>\n",
       "      <td id=\"T_c9f22_row0_col2\" class=\"data row0 col2\" >14.43</td>\n",
       "      <td id=\"T_c9f22_row0_col3\" class=\"data row0 col3\" >9.11</td>\n",
       "      <td id=\"T_c9f22_row0_col4\" class=\"data row0 col4\" >15.97</td>\n",
       "      <td id=\"T_c9f22_row0_col5\" class=\"data row0 col5\" >10.63</td>\n",
       "      <td id=\"T_c9f22_row0_col6\" class=\"data row0 col6\" >11.15</td>\n",
       "      <td id=\"T_c9f22_row0_col7\" class=\"data row0 col7\" >9.32</td>\n",
       "      <td id=\"T_c9f22_row0_col8\" class=\"data row0 col8\" >19.61</td>\n",
       "      <td id=\"T_c9f22_row0_col9\" class=\"data row0 col9\" >13.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9f22_level1_row1\" class=\"row_heading level1 row1\" >96</th>\n",
       "      <td id=\"T_c9f22_row1_col0\" class=\"data row1 col0\" >18.61</td>\n",
       "      <td id=\"T_c9f22_row1_col1\" class=\"data row1 col1\" >12.81</td>\n",
       "      <td id=\"T_c9f22_row1_col2\" class=\"data row1 col2\" >18.61</td>\n",
       "      <td id=\"T_c9f22_row1_col3\" class=\"data row1 col3\" >12.82</td>\n",
       "      <td id=\"T_c9f22_row1_col4\" class=\"data row1 col4\" >20.39</td>\n",
       "      <td id=\"T_c9f22_row1_col5\" class=\"data row1 col5\" >14.68</td>\n",
       "      <td id=\"T_c9f22_row1_col6\" class=\"data row1 col6\" >20.28</td>\n",
       "      <td id=\"T_c9f22_row1_col7\" class=\"data row1 col7\" >16.49</td>\n",
       "      <td id=\"T_c9f22_row1_col8\" class=\"data row1 col8\" >26.14</td>\n",
       "      <td id=\"T_c9f22_row1_col9\" class=\"data row1 col9\" >19.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9f22_level1_row2\" class=\"row_heading level1 row2\" >168</th>\n",
       "      <td id=\"T_c9f22_row2_col0\" class=\"data row2 col0\" >19.36</td>\n",
       "      <td id=\"T_c9f22_row2_col1\" class=\"data row2 col1\" >13.86</td>\n",
       "      <td id=\"T_c9f22_row2_col2\" class=\"data row2 col2\" >19.27</td>\n",
       "      <td id=\"T_c9f22_row2_col3\" class=\"data row2 col3\" >13.54</td>\n",
       "      <td id=\"T_c9f22_row2_col4\" class=\"data row2 col4\" >21.42</td>\n",
       "      <td id=\"T_c9f22_row2_col5\" class=\"data row2 col5\" >15.39</td>\n",
       "      <td id=\"T_c9f22_row2_col6\" class=\"data row2 col6\" >22.79</td>\n",
       "      <td id=\"T_c9f22_row2_col7\" class=\"data row2 col7\" >18.51</td>\n",
       "      <td id=\"T_c9f22_row2_col8\" class=\"data row2 col8\" >23.39</td>\n",
       "      <td id=\"T_c9f22_row2_col9\" class=\"data row2 col9\" >17.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9f22_level0_row3\" class=\"row_heading level0 row3\" rowspan=\"3\">United Kingdom</th>\n",
       "      <th id=\"T_c9f22_level1_row3\" class=\"row_heading level1 row3\" >24</th>\n",
       "      <td id=\"T_c9f22_row3_col0\" class=\"data row3 col0\" >16.11</td>\n",
       "      <td id=\"T_c9f22_row3_col1\" class=\"data row3 col1\" >10.74</td>\n",
       "      <td id=\"T_c9f22_row3_col2\" class=\"data row3 col2\" >15.65</td>\n",
       "      <td id=\"T_c9f22_row3_col3\" class=\"data row3 col3\" >10.27</td>\n",
       "      <td id=\"T_c9f22_row3_col4\" class=\"data row3 col4\" >17.64</td>\n",
       "      <td id=\"T_c9f22_row3_col5\" class=\"data row3 col5\" >12.03</td>\n",
       "      <td id=\"T_c9f22_row3_col6\" class=\"data row3 col6\" >14.41</td>\n",
       "      <td id=\"T_c9f22_row3_col7\" class=\"data row3 col7\" >12.01</td>\n",
       "      <td id=\"T_c9f22_row3_col8\" class=\"data row3 col8\" >19.89</td>\n",
       "      <td id=\"T_c9f22_row3_col9\" class=\"data row3 col9\" >14.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9f22_level1_row4\" class=\"row_heading level1 row4\" >96</th>\n",
       "      <td id=\"T_c9f22_row4_col0\" class=\"data row4 col0\" >20.17</td>\n",
       "      <td id=\"T_c9f22_row4_col1\" class=\"data row4 col1\" >14.28</td>\n",
       "      <td id=\"T_c9f22_row4_col2\" class=\"data row4 col2\" >20.02</td>\n",
       "      <td id=\"T_c9f22_row4_col3\" class=\"data row4 col3\" >14.09</td>\n",
       "      <td id=\"T_c9f22_row4_col4\" class=\"data row4 col4\" >22.50</td>\n",
       "      <td id=\"T_c9f22_row4_col5\" class=\"data row4 col5\" >16.01</td>\n",
       "      <td id=\"T_c9f22_row4_col6\" class=\"data row4 col6\" >21.53</td>\n",
       "      <td id=\"T_c9f22_row4_col7\" class=\"data row4 col7\" >17.69</td>\n",
       "      <td id=\"T_c9f22_row4_col8\" class=\"data row4 col8\" >25.54</td>\n",
       "      <td id=\"T_c9f22_row4_col9\" class=\"data row4 col9\" >19.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9f22_level1_row5\" class=\"row_heading level1 row5\" >168</th>\n",
       "      <td id=\"T_c9f22_row5_col0\" class=\"data row5 col0\" >20.85</td>\n",
       "      <td id=\"T_c9f22_row5_col1\" class=\"data row5 col1\" >14.94</td>\n",
       "      <td id=\"T_c9f22_row5_col2\" class=\"data row5 col2\" >20.62</td>\n",
       "      <td id=\"T_c9f22_row5_col3\" class=\"data row5 col3\" >14.66</td>\n",
       "      <td id=\"T_c9f22_row5_col4\" class=\"data row5 col4\" >25.91</td>\n",
       "      <td id=\"T_c9f22_row5_col5\" class=\"data row5 col5\" >18.29</td>\n",
       "      <td id=\"T_c9f22_row5_col6\" class=\"data row5 col6\" >22.67</td>\n",
       "      <td id=\"T_c9f22_row5_col7\" class=\"data row5 col7\" >18.28</td>\n",
       "      <td id=\"T_c9f22_row5_col8\" class=\"data row5 col8\" >24.69</td>\n",
       "      <td id=\"T_c9f22_row5_col9\" class=\"data row5 col9\" >18.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9f22_level0_row6\" class=\"row_heading level0 row6\" rowspan=\"3\">Spain</th>\n",
       "      <th id=\"T_c9f22_level1_row6\" class=\"row_heading level1 row6\" >24</th>\n",
       "      <td id=\"T_c9f22_row6_col0\" class=\"data row6 col0\" >10.44</td>\n",
       "      <td id=\"T_c9f22_row6_col1\" class=\"data row6 col1\" >6.66</td>\n",
       "      <td id=\"T_c9f22_row6_col2\" class=\"data row6 col2\" >9.87</td>\n",
       "      <td id=\"T_c9f22_row6_col3\" class=\"data row6 col3\" >6.07</td>\n",
       "      <td id=\"T_c9f22_row6_col4\" class=\"data row6 col4\" >17.42</td>\n",
       "      <td id=\"T_c9f22_row6_col5\" class=\"data row6 col5\" >10.45</td>\n",
       "      <td id=\"T_c9f22_row6_col6\" class=\"data row6 col6\" >9.68</td>\n",
       "      <td id=\"T_c9f22_row6_col7\" class=\"data row6 col7\" >7.89</td>\n",
       "      <td id=\"T_c9f22_row6_col8\" class=\"data row6 col8\" >14.06</td>\n",
       "      <td id=\"T_c9f22_row6_col9\" class=\"data row6 col9\" >9.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9f22_level1_row7\" class=\"row_heading level1 row7\" >96</th>\n",
       "      <td id=\"T_c9f22_row7_col0\" class=\"data row7 col0\" >14.17</td>\n",
       "      <td id=\"T_c9f22_row7_col1\" class=\"data row7 col1\" >9.59</td>\n",
       "      <td id=\"T_c9f22_row7_col2\" class=\"data row7 col2\" >13.49</td>\n",
       "      <td id=\"T_c9f22_row7_col3\" class=\"data row7 col3\" >8.79</td>\n",
       "      <td id=\"T_c9f22_row7_col4\" class=\"data row7 col4\" >21.24</td>\n",
       "      <td id=\"T_c9f22_row7_col5\" class=\"data row7 col5\" >13.74</td>\n",
       "      <td id=\"T_c9f22_row7_col6\" class=\"data row7 col6\" >14.17</td>\n",
       "      <td id=\"T_c9f22_row7_col7\" class=\"data row7 col7\" >11.22</td>\n",
       "      <td id=\"T_c9f22_row7_col8\" class=\"data row7 col8\" >19.67</td>\n",
       "      <td id=\"T_c9f22_row7_col9\" class=\"data row7 col9\" >13.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9f22_level1_row8\" class=\"row_heading level1 row8\" >168</th>\n",
       "      <td id=\"T_c9f22_row8_col0\" class=\"data row8 col0\" >14.80</td>\n",
       "      <td id=\"T_c9f22_row8_col1\" class=\"data row8 col1\" >10.05</td>\n",
       "      <td id=\"T_c9f22_row8_col2\" class=\"data row8 col2\" >14.21</td>\n",
       "      <td id=\"T_c9f22_row8_col3\" class=\"data row8 col3\" >9.48</td>\n",
       "      <td id=\"T_c9f22_row8_col4\" class=\"data row8 col4\" >21.38</td>\n",
       "      <td id=\"T_c9f22_row8_col5\" class=\"data row8 col5\" >14.08</td>\n",
       "      <td id=\"T_c9f22_row8_col6\" class=\"data row8 col6\" >17.61</td>\n",
       "      <td id=\"T_c9f22_row8_col7\" class=\"data row8 col7\" >13.59</td>\n",
       "      <td id=\"T_c9f22_row8_col8\" class=\"data row8 col8\" >17.74</td>\n",
       "      <td id=\"T_c9f22_row8_col9\" class=\"data row8 col9\" >12.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9f22_level0_row9\" class=\"row_heading level0 row9\" rowspan=\"3\">France</th>\n",
       "      <th id=\"T_c9f22_level1_row9\" class=\"row_heading level1 row9\" >24</th>\n",
       "      <td id=\"T_c9f22_row9_col0\" class=\"data row9 col0\" >10.19</td>\n",
       "      <td id=\"T_c9f22_row9_col1\" class=\"data row9 col1\" >5.94</td>\n",
       "      <td id=\"T_c9f22_row9_col2\" class=\"data row9 col2\" >9.97</td>\n",
       "      <td id=\"T_c9f22_row9_col3\" class=\"data row9 col3\" >5.66</td>\n",
       "      <td id=\"T_c9f22_row9_col4\" class=\"data row9 col4\" >11.71</td>\n",
       "      <td id=\"T_c9f22_row9_col5\" class=\"data row9 col5\" >7.17</td>\n",
       "      <td id=\"T_c9f22_row9_col6\" class=\"data row9 col6\" >7.60</td>\n",
       "      <td id=\"T_c9f22_row9_col7\" class=\"data row9 col7\" >6.06</td>\n",
       "      <td id=\"T_c9f22_row9_col8\" class=\"data row9 col8\" >12.24</td>\n",
       "      <td id=\"T_c9f22_row9_col9\" class=\"data row9 col9\" >8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9f22_level1_row10\" class=\"row_heading level1 row10\" >96</th>\n",
       "      <td id=\"T_c9f22_row10_col0\" class=\"data row10 col0\" >14.02</td>\n",
       "      <td id=\"T_c9f22_row10_col1\" class=\"data row10 col1\" >8.57</td>\n",
       "      <td id=\"T_c9f22_row10_col2\" class=\"data row10 col2\" >13.63</td>\n",
       "      <td id=\"T_c9f22_row10_col3\" class=\"data row10 col3\" >8.20</td>\n",
       "      <td id=\"T_c9f22_row10_col4\" class=\"data row10 col4\" >14.80</td>\n",
       "      <td id=\"T_c9f22_row10_col5\" class=\"data row10 col5\" >9.20</td>\n",
       "      <td id=\"T_c9f22_row10_col6\" class=\"data row10 col6\" >12.54</td>\n",
       "      <td id=\"T_c9f22_row10_col7\" class=\"data row10 col7\" >9.81</td>\n",
       "      <td id=\"T_c9f22_row10_col8\" class=\"data row10 col8\" >17.82</td>\n",
       "      <td id=\"T_c9f22_row10_col9\" class=\"data row10 col9\" >12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9f22_level1_row11\" class=\"row_heading level1 row11\" >168</th>\n",
       "      <td id=\"T_c9f22_row11_col0\" class=\"data row11 col0\" >14.58</td>\n",
       "      <td id=\"T_c9f22_row11_col1\" class=\"data row11 col1\" >9.12</td>\n",
       "      <td id=\"T_c9f22_row11_col2\" class=\"data row11 col2\" >14.26</td>\n",
       "      <td id=\"T_c9f22_row11_col3\" class=\"data row11 col3\" >8.86</td>\n",
       "      <td id=\"T_c9f22_row11_col4\" class=\"data row11 col4\" >15.14</td>\n",
       "      <td id=\"T_c9f22_row11_col5\" class=\"data row11 col5\" >9.53</td>\n",
       "      <td id=\"T_c9f22_row11_col6\" class=\"data row11 col6\" >14.89</td>\n",
       "      <td id=\"T_c9f22_row11_col7\" class=\"data row11 col7\" >11.50</td>\n",
       "      <td id=\"T_c9f22_row11_col8\" class=\"data row11 col8\" >16.29</td>\n",
       "      <td id=\"T_c9f22_row11_col9\" class=\"data row11 col9\" >10.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9f22_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"3\">Italy</th>\n",
       "      <th id=\"T_c9f22_level1_row12\" class=\"row_heading level1 row12\" >24</th>\n",
       "      <td id=\"T_c9f22_row12_col0\" class=\"data row12 col0\" >10.22</td>\n",
       "      <td id=\"T_c9f22_row12_col1\" class=\"data row12 col1\" >6.18</td>\n",
       "      <td id=\"T_c9f22_row12_col2\" class=\"data row12 col2\" >9.95</td>\n",
       "      <td id=\"T_c9f22_row12_col3\" class=\"data row12 col3\" >5.82</td>\n",
       "      <td id=\"T_c9f22_row12_col4\" class=\"data row12 col4\" >10.67</td>\n",
       "      <td id=\"T_c9f22_row12_col5\" class=\"data row12 col5\" >6.60</td>\n",
       "      <td id=\"T_c9f22_row12_col6\" class=\"data row12 col6\" >8.64</td>\n",
       "      <td id=\"T_c9f22_row12_col7\" class=\"data row12 col7\" >6.98</td>\n",
       "      <td id=\"T_c9f22_row12_col8\" class=\"data row12 col8\" >14.40</td>\n",
       "      <td id=\"T_c9f22_row12_col9\" class=\"data row12 col9\" >9.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9f22_level1_row13\" class=\"row_heading level1 row13\" >96</th>\n",
       "      <td id=\"T_c9f22_row13_col0\" class=\"data row13 col0\" >13.54</td>\n",
       "      <td id=\"T_c9f22_row13_col1\" class=\"data row13 col1\" >8.47</td>\n",
       "      <td id=\"T_c9f22_row13_col2\" class=\"data row13 col2\" >13.15</td>\n",
       "      <td id=\"T_c9f22_row13_col3\" class=\"data row13 col3\" >7.98</td>\n",
       "      <td id=\"T_c9f22_row13_col4\" class=\"data row13 col4\" >13.70</td>\n",
       "      <td id=\"T_c9f22_row13_col5\" class=\"data row13 col5\" >9.16</td>\n",
       "      <td id=\"T_c9f22_row13_col6\" class=\"data row13 col6\" >13.12</td>\n",
       "      <td id=\"T_c9f22_row13_col7\" class=\"data row13 col7\" >10.33</td>\n",
       "      <td id=\"T_c9f22_row13_col8\" class=\"data row13 col8\" >19.71</td>\n",
       "      <td id=\"T_c9f22_row13_col9\" class=\"data row13 col9\" >14.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c9f22_level1_row14\" class=\"row_heading level1 row14\" >168</th>\n",
       "      <td id=\"T_c9f22_row14_col0\" class=\"data row14 col0\" >13.97</td>\n",
       "      <td id=\"T_c9f22_row14_col1\" class=\"data row14 col1\" >8.98</td>\n",
       "      <td id=\"T_c9f22_row14_col2\" class=\"data row14 col2\" >13.75</td>\n",
       "      <td id=\"T_c9f22_row14_col3\" class=\"data row14 col3\" >8.58</td>\n",
       "      <td id=\"T_c9f22_row14_col4\" class=\"data row14 col4\" >14.53</td>\n",
       "      <td id=\"T_c9f22_row14_col5\" class=\"data row14 col5\" >9.75</td>\n",
       "      <td id=\"T_c9f22_row14_col6\" class=\"data row14 col6\" >16.57</td>\n",
       "      <td id=\"T_c9f22_row14_col7\" class=\"data row14 col7\" >12.76</td>\n",
       "      <td id=\"T_c9f22_row14_col8\" class=\"data row14 col8\" >15.69</td>\n",
       "      <td id=\"T_c9f22_row14_col9\" class=\"data row14 col9\" >10.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f24f870b6d0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Highlight the minimal values in bold.\n",
    "all_df_with_timellm = pd.concat([timellm_df_scale, all_df_with_result], axis=1)\n",
    "styled_df = style_dataframe(all_df_with_timellm, decimal_places=2, second_value=True)\n",
    "styled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save styled dataframe to HTML\n",
    "path = './stored_elements/tables'\n",
    "\n",
    "if not os.path.exists(path):\n",
    "    os.makedirs(path)\n",
    "styled_df.to_html(os.path.join(path, 'timellm_10_power_2.html'), index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Decomposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_6a21d_row0_col2, #T_6a21d_row0_col3, #T_6a21d_row1_col2, #T_6a21d_row1_col3, #T_6a21d_row2_col2, #T_6a21d_row2_col3, #T_6a21d_row3_col2, #T_6a21d_row3_col3, #T_6a21d_row4_col2, #T_6a21d_row4_col3, #T_6a21d_row5_col2, #T_6a21d_row5_col3, #T_6a21d_row6_col2, #T_6a21d_row6_col3, #T_6a21d_row7_col2, #T_6a21d_row7_col3, #T_6a21d_row8_col2, #T_6a21d_row8_col3, #T_6a21d_row9_col0, #T_6a21d_row9_col3, #T_6a21d_row10_col2, #T_6a21d_row10_col3, #T_6a21d_row11_col2, #T_6a21d_row11_col3, #T_6a21d_row12_col2, #T_6a21d_row12_col3, #T_6a21d_row13_col2, #T_6a21d_row13_col3, #T_6a21d_row14_col2, #T_6a21d_row14_col3 {\n",
       "  font-weight: bold;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_6a21d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"index_name level0\" >Model</th>\n",
       "      <th id=\"T_6a21d_level0_col0\" class=\"col_heading level0 col0\" colspan=\"2\">Decomposition</th>\n",
       "      <th id=\"T_6a21d_level0_col2\" class=\"col_heading level0 col2\" colspan=\"2\">PatchTST</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"index_name level1\" >Metrics</th>\n",
       "      <th id=\"T_6a21d_level1_col0\" class=\"col_heading level1 col0\" >RMSE</th>\n",
       "      <th id=\"T_6a21d_level1_col1\" class=\"col_heading level1 col1\" >MAE</th>\n",
       "      <th id=\"T_6a21d_level1_col2\" class=\"col_heading level1 col2\" >RMSE</th>\n",
       "      <th id=\"T_6a21d_level1_col3\" class=\"col_heading level1 col3\" >MAE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Country</th>\n",
       "      <th class=\"index_name level1\" >Pred_len</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6a21d_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"3\">Germany</th>\n",
       "      <th id=\"T_6a21d_level1_row0\" class=\"row_heading level1 row0\" >24</th>\n",
       "      <td id=\"T_6a21d_row0_col0\" class=\"data row0 col0\" >14.55</td>\n",
       "      <td id=\"T_6a21d_row0_col1\" class=\"data row0 col1\" >9.21</td>\n",
       "      <td id=\"T_6a21d_row0_col2\" class=\"data row0 col2\" >14.43</td>\n",
       "      <td id=\"T_6a21d_row0_col3\" class=\"data row0 col3\" >9.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a21d_level1_row1\" class=\"row_heading level1 row1\" >96</th>\n",
       "      <td id=\"T_6a21d_row1_col0\" class=\"data row1 col0\" >18.70</td>\n",
       "      <td id=\"T_6a21d_row1_col1\" class=\"data row1 col1\" >12.85</td>\n",
       "      <td id=\"T_6a21d_row1_col2\" class=\"data row1 col2\" >18.61</td>\n",
       "      <td id=\"T_6a21d_row1_col3\" class=\"data row1 col3\" >12.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a21d_level1_row2\" class=\"row_heading level1 row2\" >168</th>\n",
       "      <td id=\"T_6a21d_row2_col0\" class=\"data row2 col0\" >19.40</td>\n",
       "      <td id=\"T_6a21d_row2_col1\" class=\"data row2 col1\" >13.74</td>\n",
       "      <td id=\"T_6a21d_row2_col2\" class=\"data row2 col2\" >19.27</td>\n",
       "      <td id=\"T_6a21d_row2_col3\" class=\"data row2 col3\" >13.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a21d_level0_row3\" class=\"row_heading level0 row3\" rowspan=\"3\">United Kingdom</th>\n",
       "      <th id=\"T_6a21d_level1_row3\" class=\"row_heading level1 row3\" >24</th>\n",
       "      <td id=\"T_6a21d_row3_col0\" class=\"data row3 col0\" >16.04</td>\n",
       "      <td id=\"T_6a21d_row3_col1\" class=\"data row3 col1\" >10.56</td>\n",
       "      <td id=\"T_6a21d_row3_col2\" class=\"data row3 col2\" >15.65</td>\n",
       "      <td id=\"T_6a21d_row3_col3\" class=\"data row3 col3\" >10.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a21d_level1_row4\" class=\"row_heading level1 row4\" >96</th>\n",
       "      <td id=\"T_6a21d_row4_col0\" class=\"data row4 col0\" >21.02</td>\n",
       "      <td id=\"T_6a21d_row4_col1\" class=\"data row4 col1\" >14.58</td>\n",
       "      <td id=\"T_6a21d_row4_col2\" class=\"data row4 col2\" >20.02</td>\n",
       "      <td id=\"T_6a21d_row4_col3\" class=\"data row4 col3\" >14.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a21d_level1_row5\" class=\"row_heading level1 row5\" >168</th>\n",
       "      <td id=\"T_6a21d_row5_col0\" class=\"data row5 col0\" >20.82</td>\n",
       "      <td id=\"T_6a21d_row5_col1\" class=\"data row5 col1\" >14.81</td>\n",
       "      <td id=\"T_6a21d_row5_col2\" class=\"data row5 col2\" >20.62</td>\n",
       "      <td id=\"T_6a21d_row5_col3\" class=\"data row5 col3\" >14.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a21d_level0_row6\" class=\"row_heading level0 row6\" rowspan=\"3\">Spain</th>\n",
       "      <th id=\"T_6a21d_level1_row6\" class=\"row_heading level1 row6\" >24</th>\n",
       "      <td id=\"T_6a21d_row6_col0\" class=\"data row6 col0\" >9.98</td>\n",
       "      <td id=\"T_6a21d_row6_col1\" class=\"data row6 col1\" >6.22</td>\n",
       "      <td id=\"T_6a21d_row6_col2\" class=\"data row6 col2\" >9.87</td>\n",
       "      <td id=\"T_6a21d_row6_col3\" class=\"data row6 col3\" >6.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a21d_level1_row7\" class=\"row_heading level1 row7\" >96</th>\n",
       "      <td id=\"T_6a21d_row7_col0\" class=\"data row7 col0\" >13.62</td>\n",
       "      <td id=\"T_6a21d_row7_col1\" class=\"data row7 col1\" >9.02</td>\n",
       "      <td id=\"T_6a21d_row7_col2\" class=\"data row7 col2\" >13.49</td>\n",
       "      <td id=\"T_6a21d_row7_col3\" class=\"data row7 col3\" >8.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a21d_level1_row8\" class=\"row_heading level1 row8\" >168</th>\n",
       "      <td id=\"T_6a21d_row8_col0\" class=\"data row8 col0\" >14.46</td>\n",
       "      <td id=\"T_6a21d_row8_col1\" class=\"data row8 col1\" >9.67</td>\n",
       "      <td id=\"T_6a21d_row8_col2\" class=\"data row8 col2\" >14.21</td>\n",
       "      <td id=\"T_6a21d_row8_col3\" class=\"data row8 col3\" >9.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a21d_level0_row9\" class=\"row_heading level0 row9\" rowspan=\"3\">France</th>\n",
       "      <th id=\"T_6a21d_level1_row9\" class=\"row_heading level1 row9\" >24</th>\n",
       "      <td id=\"T_6a21d_row9_col0\" class=\"data row9 col0\" >9.97</td>\n",
       "      <td id=\"T_6a21d_row9_col1\" class=\"data row9 col1\" >5.69</td>\n",
       "      <td id=\"T_6a21d_row9_col2\" class=\"data row9 col2\" >9.97</td>\n",
       "      <td id=\"T_6a21d_row9_col3\" class=\"data row9 col3\" >5.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a21d_level1_row10\" class=\"row_heading level1 row10\" >96</th>\n",
       "      <td id=\"T_6a21d_row10_col0\" class=\"data row10 col0\" >13.85</td>\n",
       "      <td id=\"T_6a21d_row10_col1\" class=\"data row10 col1\" >8.38</td>\n",
       "      <td id=\"T_6a21d_row10_col2\" class=\"data row10 col2\" >13.63</td>\n",
       "      <td id=\"T_6a21d_row10_col3\" class=\"data row10 col3\" >8.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a21d_level1_row11\" class=\"row_heading level1 row11\" >168</th>\n",
       "      <td id=\"T_6a21d_row11_col0\" class=\"data row11 col0\" >14.42</td>\n",
       "      <td id=\"T_6a21d_row11_col1\" class=\"data row11 col1\" >8.96</td>\n",
       "      <td id=\"T_6a21d_row11_col2\" class=\"data row11 col2\" >14.26</td>\n",
       "      <td id=\"T_6a21d_row11_col3\" class=\"data row11 col3\" >8.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a21d_level0_row12\" class=\"row_heading level0 row12\" rowspan=\"3\">Italy</th>\n",
       "      <th id=\"T_6a21d_level1_row12\" class=\"row_heading level1 row12\" >24</th>\n",
       "      <td id=\"T_6a21d_row12_col0\" class=\"data row12 col0\" >9.96</td>\n",
       "      <td id=\"T_6a21d_row12_col1\" class=\"data row12 col1\" >5.83</td>\n",
       "      <td id=\"T_6a21d_row12_col2\" class=\"data row12 col2\" >9.95</td>\n",
       "      <td id=\"T_6a21d_row12_col3\" class=\"data row12 col3\" >5.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a21d_level1_row13\" class=\"row_heading level1 row13\" >96</th>\n",
       "      <td id=\"T_6a21d_row13_col0\" class=\"data row13 col0\" >13.34</td>\n",
       "      <td id=\"T_6a21d_row13_col1\" class=\"data row13 col1\" >8.18</td>\n",
       "      <td id=\"T_6a21d_row13_col2\" class=\"data row13 col2\" >13.15</td>\n",
       "      <td id=\"T_6a21d_row13_col3\" class=\"data row13 col3\" >7.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6a21d_level1_row14\" class=\"row_heading level1 row14\" >168</th>\n",
       "      <td id=\"T_6a21d_row14_col0\" class=\"data row14 col0\" >13.80</td>\n",
       "      <td id=\"T_6a21d_row14_col1\" class=\"data row14 col1\" >8.74</td>\n",
       "      <td id=\"T_6a21d_row14_col2\" class=\"data row14 col2\" >13.75</td>\n",
       "      <td id=\"T_6a21d_row14_col3\" class=\"data row14 col3\" >8.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f6d92897210>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decomposition_df = read_results_csv('patchtst/patchtst_decomposition.csv', \n",
    "                                    columns_to_extract=('Decomposition', ['RMSE', 'MAE'])\n",
    "                                    )\n",
    "\n",
    "#all_df = pd.concat([patchtst_df, decomposition_df], axis=1)\n",
    "\n",
    "# Change country codes to country names\n",
    "decomposition_df.index = pd.MultiIndex.from_tuples(\n",
    "    [(map_country_name(x[0]), x[1]) for x in decomposition_df.index],\n",
    "    names=level_names\n",
    ")\n",
    "\n",
    "# Scale\n",
    "decomposition_df_scale = decomposition_df * scale\n",
    "all_df = pd.concat([decomposition_df_scale, result_df], axis=1)\n",
    "\n",
    "# Highlight the minimal values in bold\n",
    "styled_df = style_dataframe(all_df, decimal_places=2)\n",
    "styled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "styled_df.to_html(os.path.join(path, 'decomposition_10_power_2.html'), index=True)"
   ]
  }
 ],
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