Create arima_forecast.py

arima_forecast.py

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+# arima_forecast.py
+import pandas as pd
+from statsmodels.tsa.arima.model import ARIMAResults
+from sklearn.metrics import mean_absolute_percentage_error
+
+def load_model(path="best_arima_model.pkl"):
+    return ARIMAResults.load(path)
+
+def load_timeseries(df, comm_code):
+    monthly_cols = [col for col in df.columns if col.startswith('INDX')]
+    df_filtered = df[df['COMM_CODE'] == comm_code]
+    ts_values = df_filtered[monthly_cols].values.flatten()
+    ts = pd.Series(ts_values, index=pd.date_range(start='2012-04-01', periods=len(monthly_cols), freq='MS'))
+    return ts
+
+def forecast(comm_code, forecast_months=6, csv_path="data.csv"):
+    df = pd.read_csv(csv_path)
+    ts = load_timeseries(df, comm_code)
+    train = ts[:-forecast_months]
+    test = ts[-forecast_months:]
+    
+    model = load_model()
+    forecast = model.forecast(steps=forecast_months)
+    forecast.index = test.index
+
+    mape = mean_absolute_percentage_error(test, forecast)
+    return forecast, test, mape