| | |
| | """ |
| | Run Time Series Analysis: Decomposition, ARIMA forecasting, plots |
| | """ |
| | import os |
| | import sys |
| | import glob |
| | sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'src')) |
| | import pandas as pd |
| | import numpy as np |
| | import matplotlib.pyplot as plt |
| | from statsmodels.tsa.seasonal import seasonal_decompose |
| | from statsmodels.tsa.arima.model import ARIMA |
| |
|
| | def find_latest_data(): |
| | data_files = glob.glob('data/processed/fred_data_*.csv') |
| | if not data_files: |
| | raise FileNotFoundError("No FRED data files found. Run the pipeline first.") |
| | return max(data_files, key=os.path.getctime) |
| |
|
| | def main(): |
| | print("="*60) |
| | print("FRED Time Series Analysis: Decomposition & ARIMA Forecasting") |
| | print("="*60) |
| | data_file = find_latest_data() |
| | print(f"Using data file: {data_file}") |
| | df = pd.read_csv(data_file, index_col=0, parse_dates=True) |
| | target_var = 'GDP' |
| | if target_var not in df.columns: |
| | print(f"Target variable '{target_var}' not found in data.") |
| | return |
| | ts_data = df[target_var].dropna() |
| | if len(ts_data) < 50: |
| | print("Insufficient data for time series analysis (need at least 50 points). Skipping.") |
| | return |
| | print(f"Time series length: {len(ts_data)} observations") |
| | print(f"Date range: {ts_data.index.min()} to {ts_data.index.max()}") |
| | |
| | try: |
| | if ts_data.index.freq is None: |
| | ts_monthly = ts_data.resample('M').mean() |
| | else: |
| | ts_monthly = ts_data |
| | decomposition = seasonal_decompose(ts_monthly, model='additive', period=12) |
| | fig, axes = plt.subplots(4, 1, figsize=(12, 10)) |
| | decomposition.observed.plot(ax=axes[0], title='Original Time Series') |
| | decomposition.trend.plot(ax=axes[1], title='Trend') |
| | decomposition.seasonal.plot(ax=axes[2], title='Seasonality') |
| | decomposition.resid.plot(ax=axes[3], title='Residuals') |
| | plt.tight_layout() |
| | plt.savefig('data/exports/time_series_decomposition.png', dpi=200, bbox_inches='tight') |
| | plt.close() |
| | print("Decomposition plot saved.") |
| | except Exception as e: |
| | print(f"Decomposition failed: {e}") |
| | |
| | try: |
| | model = ARIMA(ts_monthly, order=(1, 1, 1)) |
| | fitted_model = model.fit() |
| | print(f"ARIMA Model Summary:\n{fitted_model.summary()}") |
| | forecast_steps = min(12, len(ts_monthly) // 4) |
| | forecast = fitted_model.forecast(steps=forecast_steps) |
| | conf_int = fitted_model.get_forecast(steps=forecast_steps).conf_int() |
| | plt.figure(figsize=(12, 6)) |
| | ts_monthly.plot(label='Historical Data') |
| | forecast.plot(label='Forecast', color='red') |
| | plt.fill_between(forecast.index, conf_int.iloc[:, 0], conf_int.iloc[:, 1], alpha=0.3, color='red', label='Confidence Interval') |
| | plt.title(f'{target_var} - ARIMA Forecast') |
| | plt.legend() |
| | plt.grid(True) |
| | plt.tight_layout() |
| | plt.savefig('data/exports/time_series_forecast.png', dpi=200, bbox_inches='tight') |
| | plt.close() |
| | print("Forecast plot saved.") |
| | except Exception as e: |
| | print(f"ARIMA modeling failed: {e}") |
| | print("\nTime series analysis complete. Outputs saved to data/exports/.") |
| |
|
| | if __name__ == "__main__": |
| | main() |
