Arima_daily.ipynb

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  "metadata": {
    "colab": {
      "name": "Arima_daily.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/shaeli1996/Household_power_consumption/blob/master/Arima_daily.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "ChM6PMqDlbBP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!wget -q https://archive.ics.uci.edu/ml/machine-learning-databases/00235/household_power_consumption.zip\n",
        "!unzip -q household_power_consumption.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pGXQfnjmmA1J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "b9248cca-98e2-4000-cffb-d4803ab1f075"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from pandas.tools.plotting import autocorrelation_plot\n",
        "%matplotlib inline\n",
        "#\n",
        "df = pd.read_csv('household_power_consumption.txt',sep=';')\n",
        "#\n",
        "df['TimeStamp'] = df['Date']+' '+df['Time']\n",
        "df['TimeStamp'] = pd.to_datetime(df['TimeStamp'],format = \"%d/%m/%Y %H:%M:%S\")\n",
        "df.index = df['TimeStamp']\n",
        "df.index.name = None\n",
        "df = df.drop(['Date','Time'],axis = 1)\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (2,3,4,5,6,7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        },
        {
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          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Global_active_power</th>\n",
              "      <th>Global_reactive_power</th>\n",
              "      <th>Voltage</th>\n",
              "      <th>Global_intensity</th>\n",
              "      <th>Sub_metering_1</th>\n",
              "      <th>Sub_metering_2</th>\n",
              "      <th>Sub_metering_3</th>\n",
              "      <th>TimeStamp</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2006-12-16 17:24:00</th>\n",
              "      <td>4.216</td>\n",
              "      <td>0.418</td>\n",
              "      <td>234.840</td>\n",
              "      <td>18.400</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2006-12-16 17:24:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-16 17:25:00</th>\n",
              "      <td>5.360</td>\n",
              "      <td>0.436</td>\n",
              "      <td>233.630</td>\n",
              "      <td>23.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2006-12-16 17:25:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-16 17:26:00</th>\n",
              "      <td>5.374</td>\n",
              "      <td>0.498</td>\n",
              "      <td>233.290</td>\n",
              "      <td>23.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2006-12-16 17:26:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-16 17:27:00</th>\n",
              "      <td>5.388</td>\n",
              "      <td>0.502</td>\n",
              "      <td>233.740</td>\n",
              "      <td>23.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2006-12-16 17:27:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-16 17:28:00</th>\n",
              "      <td>3.666</td>\n",
              "      <td>0.528</td>\n",
              "      <td>235.680</td>\n",
              "      <td>15.800</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2006-12-16 17:28:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    Global_active_power Global_reactive_power  Voltage  \\\n",
              "2006-12-16 17:24:00               4.216                 0.418  234.840   \n",
              "2006-12-16 17:25:00               5.360                 0.436  233.630   \n",
              "2006-12-16 17:26:00               5.374                 0.498  233.290   \n",
              "2006-12-16 17:27:00               5.388                 0.502  233.740   \n",
              "2006-12-16 17:28:00               3.666                 0.528  235.680   \n",
              "\n",
              "                    Global_intensity Sub_metering_1 Sub_metering_2  \\\n",
              "2006-12-16 17:24:00           18.400          0.000          1.000   \n",
              "2006-12-16 17:25:00           23.000          0.000          1.000   \n",
              "2006-12-16 17:26:00           23.000          0.000          2.000   \n",
              "2006-12-16 17:27:00           23.000          0.000          1.000   \n",
              "2006-12-16 17:28:00           15.800          0.000          1.000   \n",
              "\n",
              "                     Sub_metering_3           TimeStamp  \n",
              "2006-12-16 17:24:00            17.0 2006-12-16 17:24:00  \n",
              "2006-12-16 17:25:00            16.0 2006-12-16 17:25:00  \n",
              "2006-12-16 17:26:00            17.0 2006-12-16 17:26:00  \n",
              "2006-12-16 17:27:00            17.0 2006-12-16 17:27:00  \n",
              "2006-12-16 17:28:00            17.0 2006-12-16 17:28:00  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "metadata": {
        "id": "hul0urn2mC9R",
        "colab_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "outputId": "058bfb11-a8eb-4eb0-eefb-ca82fe822db7"
      },
      "cell_type": "code",
      "source": [
        "#@title Default title text\n",
        "df = df.convert_objects(convert_numeric=True)\n",
        "df = df.ffill()\n",
        "#\n",
        "newdf = df[['Global_active_power']]\n",
        "newdf.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: convert_objects is deprecated.  To re-infer data dtypes for object columns, use DataFrame.infer_objects()\n",
            "For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Global_active_power</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2006-12-16 17:24:00</th>\n",
              "      <td>4.216</td>\n",
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              "    <tr>\n",
              "      <th>2006-12-16 17:25:00</th>\n",
              "      <td>5.360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-16 17:26:00</th>\n",
              "      <td>5.374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-16 17:27:00</th>\n",
              "      <td>5.388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-16 17:28:00</th>\n",
              "      <td>3.666</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                     Global_active_power\n",
              "2006-12-16 17:24:00                4.216\n",
              "2006-12-16 17:25:00                5.360\n",
              "2006-12-16 17:26:00                5.374\n",
              "2006-12-16 17:27:00                5.388\n",
              "2006-12-16 17:28:00                3.666"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "id": "K1_onmt2mK4w",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df_daily = pd.DataFrame(newdf.Global_active_power.resample('d').sum())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5LWHAoYvmMVB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "069fb2b2-4524-4a77-fa5c-a955cfb606eb"
      },
      "cell_type": "code",
      "source": [
        "df_daily.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
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              "      <th>Global_active_power</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2006-12-16</th>\n",
              "      <td>1209.176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-17</th>\n",
              "      <td>3390.460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-18</th>\n",
              "      <td>2203.826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-19</th>\n",
              "      <td>1666.194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-12-20</th>\n",
              "      <td>2225.748</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Global_active_power\n",
              "2006-12-16             1209.176\n",
              "2006-12-17             3390.460\n",
              "2006-12-18             2203.826\n",
              "2006-12-19             1666.194\n",
              "2006-12-20             2225.748"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
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        "outputId": "e4ce1f44-a576-48a3-acf0-b6fb03892770"
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      "cell_type": "code",
      "source": [
        "df_daily.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1442, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
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    {
      "metadata": {
        "id": "mqN16SWUmRys",
        "colab_type": "code",
        "colab": {
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        },
        "outputId": "b393045c-6605-41b1-e6a4-b2d217103150"
      },
      "cell_type": "code",
      "source": [
        "type(df_daily)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "3kwmQ5LemU89",
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        },
        "outputId": "facc42bf-bed2-4fbe-e5c4-ae6e5f6c4fdf"
      },
      "cell_type": "code",
      "source": [
        "df_daily.info()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 1442 entries, 2006-12-16 to 2010-11-26\n",
            "Freq: D\n",
            "Data columns (total 1 columns):\n",
            "Global_active_power    1442 non-null float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 22.5 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "55HieqcEmXoj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "961f1ac5-326b-46cb-8789-2f3e0f3179d1"
      },
      "cell_type": "code",
      "source": [
        "df_daily.describe()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Global_active_power</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1442.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1563.162724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>603.833832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>210.240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1166.661500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1542.486000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1893.959000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4773.386000</td>\n",
              "    </tr>\n",
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            ],
            "text/plain": [
              "       Global_active_power\n",
              "count          1442.000000\n",
              "mean           1563.162724\n",
              "std             603.833832\n",
              "min             210.240000\n",
              "25%            1166.661500\n",
              "50%            1542.486000\n",
              "75%            1893.959000\n",
              "max            4773.386000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
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    },
    {
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        "id": "JMDshyYLmaNo",
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          "height": 286
        },
        "outputId": "cb170f8a-6b93-4d3a-f76a-78adf48f24d7"
      },
      "cell_type": "code",
      "source": [
        "df_daily.plot()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb0239cb320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
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vWesdD35whLB29si0Jdz/UvIMSSn8Gqxa/GZ4vF4ONCX34WCU98WOjp04srdD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\nLEMKv0QikWQZ/x9jIbRl0DunvgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb023579e80>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "AVrP3N2LmcrV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "7362d9a5-e5d3-4491-ed12-4aa5e831707f"
      },
      "cell_type": "code",
      "source": [
        "df_daily.hist()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fb0235a3a90>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb0236f07b8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "__5mXCPnmgct",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "e251e0ed-f019-4c71-fe9b-8c14dce54fdb"
      },
      "cell_type": "code",
      "source": [
        "autocorrelation_plot(df_daily)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: 'pandas.tools.plotting.autocorrelation_plot' is deprecated, import 'pandas.plotting.autocorrelation_plot' instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb023992518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bTyjoMZyuh54Ycl5Ou8ZbJtwkjTNdHNNcDWW0aPtcNdQuWLCk8YMaMW32Wl4P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\njDGu/X87JbswQb9OLQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb02457cb70>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "4NvGl9kAmi4q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "45bab068-112a-413f-e505-129659b4269e"
      },
      "cell_type": "code",
      "source": [
        "#comparison of mean and variance by splitting in two parts\n",
        "X = df_daily.values\n",
        "split = len(X)//2\n",
        "X1, X2 = X[0:split],X[split:]\n",
        "mean1,mean2 = X1.mean(),X2.mean()\n",
        "var1,var2 = X1.var(),X2.var()\n",
        "print(\"mean1 = %f , mean2 = %f\"%(mean1,mean2))\n",
        "print(\"var1 = %f , var2 = %f\"%(var1,var2))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mean1 = 1586.269229 , mean2 = 1540.056219\n",
            "var1 = 461000.281161 , var2 = 266656.783069\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "A_Nz6WNJmwNc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "e7dd80b2-1aa9-4058-ffde-d935a9353d3b"
      },
      "cell_type": "code",
      "source": [
        "#comparison of mean and variance by splitting in three parts\n",
        "X = df_daily.values\n",
        "split = len(X)//3\n",
        "X1,X2,X3 = X[0:split],X[split:2*split],X[2*split:]\n",
        "print(X1.shape)\n",
        "print(X2.shape)\n",
        "print(X3.shape)\n",
        "mean1,mean2,mean3 = X1.mean(),X2.mean(),X3.mean()\n",
        "var1,var2,var3 = X1.var(),X2.var(),X3.var()\n",
        "print(\"mean1 = %f , mean2 = %f , mean3 = %f\"%(mean1,mean2,mean3))\n",
        "print(\"var1 = %f , var2 = %f , var3 = %f\"%(var1,var2,var3))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(480, 1)\n",
            "(480, 1)\n",
            "(482, 1)\n",
            "mean1 = 1689.807383 , mean2 = 1470.886679 , mean3 = 1528.936718\n",
            "var1 = 486319.788623 , var2 = 328638.038413 , var3 = 252864.039091\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-A5BlYdsmyWk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "cf721b79-f52d-4649-e006-d8940e5bb142"
      },
      "cell_type": "code",
      "source": [
        "#comparison of mean and variance by splitting in four parts\n",
        "X = df_daily.values\n",
        "split = len(X)//4\n",
        "X1,X2,X3,X4 = X[0:split],X[split:2*split],X[2*split:3*split],X[3*split:]\n",
        "print(X1.shape)\n",
        "print(X2.shape)\n",
        "print(X3.shape)\n",
        "print(X4.shape)\n",
        "mean1,mean2,mean3,mean4 = X1.mean(),X2.mean(),X3.mean(),X4.mean()\n",
        "var1,var2,var3,var4 = X1.var(),X2.var(),X3.var(),X4.var()\n",
        "print(\"mean1 = %f , mean2 = %f , mean3 = %f , mean4 = %f\"%(mean1,mean2,mean3,mean4))\n",
        "print(\"var1 = %f , var2 = %f , var3 = %f , var4 = %f\"%(var1,var2,var3,var4))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(360, 1)\n",
            "(360, 1)\n",
            "(360, 1)\n",
            "(362, 1)\n",
            "mean1 = 1601.446694 , mean2 = 1569.779683 , mean3 = 1530.857450 , mean4 = 1550.636657\n",
            "var1 = 527146.185561 , var2 = 395012.913187 , var3 = 276684.790267 , var4 = 256494.297888\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "gQnlcuO9m09k",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X = df_daily['Global_active_power'].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kSoL-e5xm5wv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ad3f914b-20fd-478c-80e6-21b5aa786a92"
      },
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1209.176 3390.46  2203.826 ... 1796.248 1431.164 1488.104]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "t1OnlkGjm7_4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "7ed6be30-a7e5-4bc4-d2bb-040ded64ee85"
      },
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.stattools import adfuller"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
            "  from pandas.core import datetools\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "uj84x-ECm-Ki",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "result = adfuller(X, maxlag=None, autolag='BIC', regression='c')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_FM4HUgBnAc7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "0bea3ff7-efab-4f27-cd72-492fe3d92151"
      },
      "cell_type": "code",
      "source": [
        "print('ADF Statistic: %f' % result[0])\n",
        "print('p-value: %f' % result[1])\n",
        "print('Critical Values:')\n",
        "for key, value in result[4].items():\n",
        "\tprint('\\t%s: %.3f' % (key, value))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ADF Statistic: -4.131210\n",
            "p-value: 0.000860\n",
            "Critical Values:\n",
            "\t1%: -3.435\n",
            "\t5%: -2.864\n",
            "\t10%: -2.568\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qMLJtWQSnDGI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.arima_model import ARIMA\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vu267o0pnImY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def evaluate_ARIMA_model(df,ratio,ordr):\n",
        "  X = df[['Global_active_power']].values\n",
        "  #normalize here\n",
        "  X = X.reshape(len(X),1)\n",
        "  scaler = MinMaxScaler(feature_range = (0,1))\n",
        "  scaler = scaler.fit(X)\n",
        "  X = scaler.transform(X)\n",
        "#   X = X.values\n",
        "  #normalize finsih\n",
        "  split = int((X.shape[0])*ratio)\n",
        "  train = X[:split]\n",
        "  test = X[split:]\n",
        "  print(train.shape)\n",
        "  print(test.shape)\n",
        "  #\n",
        "  history = [x for x in train]\n",
        "  predictions = list()\n",
        "  for t in range(len(test)):\n",
        "    model = ARIMA(history,order=ordr)\n",
        "    model_fit = model.fit(disp = 0)\n",
        "    output = model_fit.forecast()\n",
        "    yhat = output[0]\n",
        "    predictions.append(yhat)\n",
        "    obs = test[t]\n",
        "    history.append(obs)\n",
        "  error = mean_squared_error(test,predictions)\n",
        "  print(\"ARIMA(\",ordr,\") error = \",error)\n",
        "#   predictions = predictions.reshape(len(predictions),1)\n",
        "  predictions = scaler.inverse_transform(predictions)\n",
        "  test = scaler.inverse_transform(test)\n",
        "#   predictions = predictions.values\n",
        "  return (error,test,predictions)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Kccz7tU2nLI4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def parameter_tuning(df,p_max,d_max,q_max):\n",
        "  best_error=9999999999\n",
        "  for i in range(p_max):\n",
        "    for j in range(d_max):\n",
        "      for k in range(q_max):\n",
        "        order = (i,j,k)\n",
        "        val = evaluate_ARIMA_model(df,0.7,order)\n",
        "        if(val[0]<best_error):\n",
        "          best_error = val[0]\n",
        "          best_test = val[1]\n",
        "          best_predictions = val[2]\n",
        "          best_order = order\n",
        "  print(\"best order = \",best_order)\n",
        "  return (best_error,best_test,best_predictions,best_order)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "c8t8PjrunNzy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1482
        },
        "outputId": "dfbae9cc-a11a-4d23-e3a5-3ab8bfbe2fde"
      },
      "cell_type": "code",
      "source": [
        "ans = parameter_tuning(df_daily,2,2,1)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1009, 1)\n",
            "(433, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA( (0, 0, 0) ) error =  0.01171916295155605\n",
            "(1009, 1)\n",
            "(433, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA( (0, 1, 0) ) error =  0.009834739368403874\n",
            "(1009, 1)\n",
            "(433, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
            "  if issubdtype(paramsdtype, float):\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
            "  elif issubdtype(paramsdtype, complex):\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
            "  if issubdtype(paramsdtype, float):\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA( (1, 0, 0) ) error =  0.007780658636070295\n",
            "(1009, 1)\n",
            "(433, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA( (1, 1, 0) ) error =  0.00847056032160667\n",
            "best order =  (1, 0, 0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Q9OA_88CnQfU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3ebc7516-2110-4279-9f26-e107a442843c"
      },
      "cell_type": "code",
      "source": [
        "print(\"Mean Square Error = %f\"%(ans[0]))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Square Error = 0.007781\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ZWaXW4tcnTDA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "2d5d203f-af34-4cc0-d152-44dcb61c831a"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(ans[1])\n",
        "plt.plot(ans[2],'r')\n",
        "plt.legend\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fsz73AqiFacifsJY9CdO4HwRgoaZpfZqm5QC8AeAwAJ8C8Ki17YvWshGBIktV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\nwfn/VyejbiXv6EAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb01e5dce48>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "3652dJu0nVhE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "outputId": "c48539cb-15ff-4292-a005-a9e893324375"
      },
      "cell_type": "code",
      "source": [
        "#graph for first 100 days\n",
        "anstp = ans = parameter_tuning(df_daily[:100],2,2,1)\n",
        "plt.plot(ans[1])\n",
        "plt.plot(ans[2],'r')\n",
        "plt.legend\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(70, 1)\n",
            "(30, 1)\n",
            "ARIMA( (0, 0, 0) ) error =  0.057620987178068614\n",
            "(70, 1)\n",
            "(30, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA( (0, 1, 0) ) error =  0.028839010134984886\n",
            "(70, 1)\n",
            "(30, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
            "  if issubdtype(paramsdtype, float):\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
            "  elif issubdtype(paramsdtype, complex):\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
            "  if issubdtype(paramsdtype, float):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "ARIMA( (1, 0, 0) ) error =  0.04670673781491132\n",
            "(70, 1)\n",
            "(30, 1)\n",
            "ARIMA( (1, 1, 0) ) error =  0.03282358403876298\n",
            "best order =  (0, 1, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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46efkflGnE722LuN4wmSwX9dSTlGBM2tm7zhm1uu1FSuJb0oE+30zD/bhaJyR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\n209Tm55yXy9SODwnPvbZWN1YiiRNDDPJNX4wtu0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hhB0cnezdLhfXNdXvP0tYv695+FHC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\nYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb01e550cf8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "QFMUY-GMp_xy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}