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docs/source/configuration.md

@@ -1,10 +1,66 @@
-# Configuration
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from datetime import datetime, timedelta
+from statsmodels.tsa.holtwinters import ExponentialSmoothing
 
-```{toctree}
-:caption: Configuration
-:maxdepth: 1
+# Step 1: Load and prepare data
+data = {
+    "Dates": [
+        "10/31/20", "11/30/20", "12/31/20", "1/31/21", "2/28/21", "3/31/21",
+        "4/30/21", "5/31/21", "6/30/21", "7/31/21", "8/31/21", "9/30/21",
+        "10/31/21", "11/30/21", "12/31/21", "1/31/22", "2/28/22", "3/31/22",
+        "4/30/22", "5/31/22", "6/30/22", "7/31/22", "8/31/22", "9/30/22",
+        "10/31/22", "11/30/22", "12/31/22", "1/31/23", "2/28/23", "3/31/23",
+        "4/30/23", "5/31/23", "6/30/23", "7/31/23", "8/31/23", "9/30/23",
+        "10/31/23", "11/30/23", "12/31/23", "1/31/24", "2/29/24", "3/31/24",
+        "4/30/24", "5/31/24", "6/30/24", "7/31/24", "8/31/24", "9/30/24"
+    ],
+    "Prices": [
+        10.1, 10.3, 11.0, 10.9, 10.9, 10.9, 10.4, 9.84, 10.0, 10.1, 10.3, 10.2,
+        10.1, 11.2, 11.4, 11.5, 11.8, 11.5, 10.7, 10.7, 10.4, 10.5, 10.4, 10.8,
+        11.0, 11.6, 11.6, 12.1, 11.7, 12.0, 11.5, 11.2, 10.9, 11.4, 11.1, 11.5,
+        11.8, 12.2, 12.8, 12.6, 12.4, 12.7, 12.1, 11.4, 11.5, 11.6, 11.5, 11.8
+    ]
+}
 
-configuring/config_overview
-Security <https://jupyter-server.readthedocs.io/en/stable/operators/security.html>
-extending/index.rst
-```
+df = pd.DataFrame(data)
+df['Dates'] = pd.to_datetime(df['Dates'])
+df.set_index('Dates', inplace=True)
+
+# Step 2: Time Series Analysis
+model = ExponentialSmoothing(df['Prices'], seasonal='add', seasonal_periods=12).fit()
+
+# Step 3: Estimation Function
+def estimate_price(input_date):
+    input_date = pd.to_datetime(input_date)
+    # Predict future values
+    future_dates = pd.date_range(start=df.index[-1] + timedelta(days=1), periods=12, freq='M')
+    future_prices = model.forecast(steps=12)
+
+    if input_date < df.index[0]:
+        # Extrapolate backwards (not typically recommended)
+        past_index = df.index[df.index <= input_date]
+        return df['Prices'][past_index[-1]]  # last available price before input date
+    elif input_date > df.index[-1]:
+        # Extrapolate forward
+        return future_prices.loc[input_date.strftime('%Y-%m-%d')]
+    else:
+        # If the date is in the existing range, return the actual price
+        return df['Prices'].loc[input_date]
+
+# Step 4: Visualization
+plt.figure(figsize=(12, 6))
+plt.plot(df.index, df['Prices'], label='Historical Prices', marker='o')
+plt.plot(future_dates, future_prices, label='Forecasted Prices', marker='x', color='orange')
+plt.xlabel('Date')
+plt.ylabel('Price')
+plt.title('Natural Gas Prices Over Time')
+plt.legend()
+plt.grid()
+plt.show()
+
+# Example Usage
+input_date = '11/30/2024'
+estimated_price = estimate_price(input_date)
+print(f'Estimated price for {input_date}: {estimated_price:.2f}')