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#!/usr/bin/env python
"""
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()}")
# Decomposition
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}")
# ARIMA Forecasting
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() |