Create sarima.py

algo/sarima.py

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+import pandas as pd
+from statsmodels.tsa.statespace.sarimax import SARIMAX
+
+def sarima_forecast(data, forecast_horizon, order=(1, 1, 1), seasonal_order=(1, 1, 1, 12)):
+    """
+    Forecast future values using a SARIMA model with a dynamic forecast horizon.
+
+    Parameters:
+    - data: Pandas Series of historical closing prices.
+    - forecast_horizon: Integer specifying the number of days to forecast.
+    - order: The (p, d, q) order of the model for the number of AR parameters, differences, and MA parameters.
+    - seasonal_order: The (P, D, Q, s) seasonal order of the model.
+
+    Returns:
+    - Pandas Series containing the forecasted values with a datetime index.
+    """
+    # Fit the SARIMA model
+    model = SARIMAX(data, order=order, seasonal_order=seasonal_order, enforce_stationarity=False, enforce_invertibility=False)
+    model_fit = model.fit(disp=False)
+    
+    # Forecast for the specified horizon
+    forecast = model_fit.forecast(steps=forecast_horizon)
+    
+    # Create a pandas Series for the forecasted values with a date index
+    future_dates = pd.date_range(start=data.index[-1] + pd.Timedelta(days=1), periods=forecast_horizon)
+    forecast_series = pd.Series(forecast, index=future_dates)
+    
+    return forecast_series