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"""
Economic Forecasting Module
Advanced time series forecasting for economic indicators using ARIMA/ETS models
"""
import logging
import warnings
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
from scipy import stats
from sklearn.metrics import mean_absolute_error, mean_squared_error
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller
logger = logging.getLogger(__name__)
class EconomicForecaster:
"""
Advanced economic forecasting using ARIMA and ETS models
with comprehensive backtesting and performance evaluation
"""
def __init__(self, data: pd.DataFrame):
"""
Initialize forecaster with economic data
Args:
data: DataFrame with economic indicators (GDPC1, INDPRO, RSAFS, etc.)
"""
self.data = data.copy()
self.forecasts = {}
self.backtest_results = {}
self.model_performance = {}
def prepare_data(self, target_series: str, frequency: str = 'Q', for_arima: bool = True) -> pd.Series:
"""
Prepare time series data for forecasting or analysis.
Args:
target_series: Series name to forecast
frequency: Data frequency ('Q' for quarterly, 'M' for monthly)
for_arima: If True, returns raw levels for ARIMA; if False, returns growth rate
Returns:
Prepared time series
"""
if target_series not in self.data.columns:
raise ValueError(f"Series {target_series} not found in data")
series = self.data[target_series].dropna()
# Ensure time-based index
if not isinstance(series.index, pd.DatetimeIndex):
raise ValueError("Index must be datetime type")
# Resample to desired frequency
if frequency == 'Q':
series = series.resample('Q').mean()
elif frequency == 'M':
series = series.resample('M').mean()
# Only use growth rates if for_arima is False
if not for_arima and target_series in ['GDPC1', 'INDPRO', 'RSAFS']:
series = series.pct_change().dropna()
return series
def check_stationarity(self, series: pd.Series) -> Dict:
"""
Perform Augmented Dickey-Fuller test for stationarity
Args:
series: Time series to test
Returns:
Dictionary with test results
"""
result = adfuller(series.dropna())
return {
'adf_statistic': result[0],
'p_value': result[1],
'critical_values': result[4],
'is_stationary': result[1] < 0.05
}
def decompose_series(self, series: pd.Series, period: int = 4) -> Dict:
"""
Decompose time series into trend, seasonal, and residual components
Args:
series: Time series to decompose
period: Seasonal period (4 for quarterly, 12 for monthly)
Returns:
Dictionary with decomposition components
"""
decomposition = seasonal_decompose(series.dropna(), period=period, extrapolate_trend='freq')
return {
'trend': decomposition.trend,
'seasonal': decomposition.seasonal,
'residual': decomposition.resid,
'observed': decomposition.observed
}
def fit_arima_model(self, series: pd.Series, order: Tuple[int, int, int] = None) -> ARIMA:
"""
Fit ARIMA model to time series using raw levels (not growth rates)
Args:
series: Time series data (raw levels)
order: ARIMA order (p, d, q). If None, auto-detect
Returns:
Fitted ARIMA model
"""
# Ensure we're working with raw levels, not growth rates
if series.isna().any():
series = series.dropna()
# Ensure series has enough data points
if len(series) < 10:
raise ValueError("Series must have at least 10 data points for ARIMA fitting")
if order is None:
# Auto-detect order using AIC minimization with improved search
best_aic = np.inf
best_order = (1, 1, 1)
# Improved order search that avoids degenerate models
# Start with more reasonable orders to avoid ARIMA(0,0,0)
search_orders = [
(1, 1, 1), (2, 1, 1), (1, 1, 2), (2, 1, 2), # Common orders
(0, 1, 1), (1, 0, 1), (1, 1, 0), # Simple orders
(2, 0, 1), (1, 0, 2), (2, 1, 0), # Alternative orders
(3, 1, 1), (1, 1, 3), (2, 2, 1), (1, 2, 2), # Higher orders
]
for p, d, q in search_orders:
try:
model = ARIMA(series, order=(p, d, q))
fitted_model = model.fit()
# Check if model is degenerate (all parameters near zero)
params = fitted_model.params
if len(params) > 0:
# Skip models where all AR/MA parameters are very small
ar_params = params[1:p+1] if p > 0 else []
ma_params = params[p+1:p+1+q] if q > 0 else []
# Check if model is essentially a random walk or constant
if (p == 0 and d == 0 and q == 0) or \
(p == 0 and d == 1 and q == 0) or \
(len(ar_params) > 0 and all(abs(p) < 0.01 for p in ar_params)) or \
(len(ma_params) > 0 and all(abs(p) < 0.01 for p in ma_params)):
logger.debug(f"Skipping degenerate ARIMA({p},{d},{q})")
continue
if fitted_model.aic < best_aic:
best_aic = fitted_model.aic
best_order = (p, d, q)
logger.debug(f"New best ARIMA({p},{d},{q}) with AIC: {best_aic}")
except Exception as e:
logger.debug(f"ARIMA({p},{d},{q}) failed: {e}")
continue
order = best_order
logger.info(f"Auto-detected ARIMA order: {order} with AIC: {best_aic}")
# If we still have a degenerate model, force a reasonable order
if order == (0, 0, 0) or order == (0, 1, 0):
logger.warning("Detected degenerate ARIMA order, forcing to ARIMA(1,1,1)")
order = (1, 1, 1)
try:
model = ARIMA(series, order=order)
fitted_model = model.fit()
# Debug: Log model parameters
logger.info(f"ARIMA model fitted successfully with AIC: {fitted_model.aic}")
logger.info(f"ARIMA order: {order}")
logger.info(f"Model parameters: {fitted_model.params}")
return fitted_model
except Exception as e:
logger.warning(f"ARIMA fitting failed with order {order}: {e}")
# Try fallback orders
fallback_orders = [(1, 1, 1), (0, 1, 1), (1, 0, 1), (1, 1, 0)]
for fallback_order in fallback_orders:
try:
model = ARIMA(series, order=fallback_order)
fitted_model = model.fit()
logger.info(f"ARIMA fallback model fitted with order {fallback_order}")
return fitted_model
except Exception as fallback_e:
logger.debug(f"Fallback ARIMA{fallback_order} failed: {fallback_e}")
continue
# Last resort: simple moving average
logger.warning("All ARIMA models failed, using simple moving average")
raise ValueError("Unable to fit any ARIMA model to the data")
def fit_ets_model(self, series: pd.Series, seasonal_periods: int = 4) -> ExponentialSmoothing:
"""
Fit ETS (Exponential Smoothing) model to time series
Args:
series: Time series data
seasonal_periods: Number of seasonal periods
Returns:
Fitted ETS model
"""
model = ExponentialSmoothing(
series,
seasonal_periods=seasonal_periods,
trend='add',
seasonal='add'
)
fitted_model = model.fit()
return fitted_model
def forecast_series(self, series: pd.Series, model_type: str = 'auto',
forecast_periods: int = 4) -> Dict:
"""
Forecast time series using specified model
Args:
series: Time series to forecast
model_type: 'arima', 'ets', or 'auto'
forecast_periods: Number of periods to forecast
Returns:
Dictionary with forecast results
"""
if model_type == 'auto':
# Try both models and select the one with better AIC
try:
arima_model = self.fit_arima_model(series)
arima_aic = arima_model.aic
except:
arima_aic = np.inf
try:
ets_model = self.fit_ets_model(series)
ets_aic = ets_model.aic
except:
ets_aic = np.inf
if arima_aic < ets_aic:
model_type = 'arima'
model = arima_model
else:
model_type = 'ets'
model = ets_model
elif model_type == 'arima':
model = self.fit_arima_model(series)
elif model_type == 'ets':
model = self.fit_ets_model(series)
else:
raise ValueError("model_type must be 'arima', 'ets', or 'auto'")
# Generate forecast using proper method for each model type
if model_type == 'arima':
# Use get_forecast() for ARIMA to get proper confidence intervals
forecast_result = model.get_forecast(steps=forecast_periods)
forecast = forecast_result.predicted_mean
try:
forecast_ci = forecast_result.conf_int()
# Check if confidence intervals are valid (not all NaN)
if forecast_ci.isna().all().all() or forecast_ci.empty:
# Improved fallback confidence intervals
forecast_ci = self._calculate_improved_confidence_intervals(forecast, series, model)
else:
# Ensure confidence intervals have proper column names
if len(forecast_ci.columns) >= 2:
forecast_ci.columns = ['lower', 'upper']
else:
# Improved fallback if column structure is unexpected
forecast_ci = self._calculate_improved_confidence_intervals(forecast, series, model)
# Debug: Log confidence intervals
logger.info(f"ARIMA confidence intervals: {forecast_ci.to_dict()}")
# Check if confidence intervals are too wide and provide warning
ci_widths = forecast_ci['upper'] - forecast_ci['lower']
mean_width = ci_widths.mean()
forecast_mean = forecast.mean()
relative_width = mean_width / abs(forecast_mean) if abs(forecast_mean) > 0 else 0
if relative_width > 0.5: # If confidence interval is more than 50% of forecast value
logger.warning(f"Confidence intervals are very wide (relative width: {relative_width:.2%})")
logger.info("This may indicate high uncertainty or model instability")
except Exception as e:
logger.warning(f"ARIMA confidence interval calculation failed: {e}")
# Improved fallback confidence intervals
forecast_ci = self._calculate_improved_confidence_intervals(forecast, series, model)
else:
# For ETS, use forecast() method
forecast = model.forecast(steps=forecast_periods)
# Use improved confidence intervals for ETS
forecast_ci = self._calculate_improved_confidence_intervals(forecast, series, model)
# Debug: Log final results
logger.info(f"Final forecast is flat: {len(set(forecast)) == 1}")
logger.info(f"Forecast type: {type(forecast)}")
return {
'model': model,
'model_type': model_type,
'forecast': forecast,
'confidence_intervals': forecast_ci,
'aic': model.aic if hasattr(model, 'aic') else None
}
def _calculate_improved_confidence_intervals(self, forecast: pd.Series, series: pd.Series, model) -> pd.DataFrame:
"""
Calculate improved confidence intervals with better uncertainty quantification
Args:
forecast: Forecast values
series: Original time series
model: Fitted model
Returns:
DataFrame with improved confidence intervals
"""
try:
# Calculate forecast errors from model residuals if available
if hasattr(model, 'resid') and len(model.resid) > 0:
# Use model residuals for more accurate uncertainty
residuals = model.resid.dropna()
forecast_std = residuals.std()
# Adjust for forecast horizon (uncertainty increases with horizon)
horizon_factors = np.sqrt(np.arange(1, len(forecast) + 1))
confidence_intervals = []
for i, (fcast, factor) in enumerate(zip(forecast, horizon_factors)):
# Use 95% confidence interval (1.96 * std)
margin = 1.96 * forecast_std * factor
lower = fcast - margin
upper = fcast + margin
confidence_intervals.append({'lower': lower, 'upper': upper})
return pd.DataFrame(confidence_intervals, index=forecast.index)
else:
# Fallback to series-based uncertainty
series_std = series.std()
# Use a more conservative approach for economic data
# Economic forecasts typically have higher uncertainty
uncertainty_factor = 1.5 # Adjust based on data characteristics
confidence_intervals = []
for i, fcast in enumerate(forecast):
# Increase uncertainty with forecast horizon
horizon_factor = 1 + (i * 0.1) # 10% increase per period
margin = 1.96 * series_std * uncertainty_factor * horizon_factor
lower = fcast - margin
upper = fcast + margin
confidence_intervals.append({'lower': lower, 'upper': upper})
return pd.DataFrame(confidence_intervals, index=forecast.index)
except Exception as e:
logger.warning(f"Improved confidence interval calculation failed: {e}")
# Ultimate fallback
series_std = series.std()
return pd.DataFrame({
'lower': forecast - 1.96 * series_std,
'upper': forecast + 1.96 * series_std
}, index=forecast.index)
def backtest_forecast(self, series: pd.Series, model_type: str = 'auto',
train_size: float = 0.8, test_periods: int = 8) -> Dict:
"""
Perform backtesting of forecasting models
Args:
series: Time series to backtest
model_type: Model type to use
train_size: Proportion of data for training
test_periods: Number of periods to test
Returns:
Dictionary with backtest results
"""
n = len(series)
train_end = int(n * train_size)
actual_values = []
predicted_values = []
errors = []
for i in range(test_periods):
if train_end + i >= n:
break
# Use expanding window
train_data = series.iloc[:train_end + i]
test_value = series.iloc[train_end + i]
try:
forecast_result = self.forecast_series(train_data, model_type, 1)
prediction = forecast_result['forecast'].iloc[0]
actual_values.append(test_value)
predicted_values.append(prediction)
errors.append(test_value - prediction)
except Exception as e:
logger.warning(f"Forecast failed at step {i}: {e}")
continue
if not actual_values:
return {'error': 'No successful forecasts generated'}
# Calculate performance metrics
mae = mean_absolute_error(actual_values, predicted_values)
mse = mean_squared_error(actual_values, predicted_values)
rmse = np.sqrt(mse)
# Use safe MAPE calculation to avoid division by zero
actual_array = np.array(actual_values)
predicted_array = np.array(predicted_values)
denominator = np.maximum(np.abs(actual_array), 1e-8)
mape = np.mean(np.abs((actual_array - predicted_array) / denominator)) * 100
return {
'actual_values': actual_values,
'predicted_values': predicted_values,
'errors': errors,
'mae': mae,
'mse': mse,
'rmse': rmse,
'mape': mape,
'test_periods': len(actual_values)
}
def forecast_economic_indicators(self, indicators: List[str] = None) -> Dict:
"""
Forecast multiple economic indicators
Args:
indicators: List of indicators to forecast. If None, use default set
Returns:
Dictionary with forecasts for all indicators
"""
if indicators is None:
indicators = ['GDPC1', 'INDPRO', 'RSAFS']
results = {}
for indicator in indicators:
try:
# Prepare raw data for forecasting (use raw levels, not growth rates)
series = self.prepare_data(indicator, for_arima=True)
# Prepare growth rates for analysis
growth_series = self.prepare_data(indicator, for_arima=False)
# Check stationarity on growth rates
stationarity = self.check_stationarity(growth_series)
# Decompose growth rates
decomposition = self.decompose_series(growth_series)
# Generate forecast using raw levels
forecast_result = self.forecast_series(series)
# Perform backtest on raw levels
backtest_result = self.backtest_forecast(series)
results[indicator] = {
'stationarity': stationarity,
'decomposition': decomposition,
'forecast': forecast_result,
'backtest': backtest_result,
'raw_series': series,
'growth_series': growth_series
}
logger.info(f"Successfully forecasted {indicator}")
except Exception as e:
logger.error(f"Failed to forecast {indicator}: {e}")
results[indicator] = {'error': str(e)}
return results
def generate_forecast_report(self, forecast_result, periods=None):
"""
Generate a markdown table for forecast results.
Args:
forecast_result: dict with keys 'forecast', 'confidence_intervals'
periods: list of period labels (optional)
Returns:
Markdown string
"""
forecast = forecast_result.get('forecast')
ci = forecast_result.get('confidence_intervals')
if forecast is None or ci is None:
return 'No forecast results available.'
if periods is None:
periods = [f"Period {i+1}" for i in range(len(forecast))]
lines = ["| Period | Forecast | 95% CI Lower | 95% CI Upper |", "| ------- | ------------- | ------------ | ------------ |"]
for i, (f, p) in enumerate(zip(forecast, periods)):
try:
lower = ci.iloc[i, 0] if hasattr(ci, 'iloc') else ci[i][0]
upper = ci.iloc[i, 1] if hasattr(ci, 'iloc') else ci[i][1]
except Exception:
lower = upper = 'N/A'
lines.append(f"| {p} | **{f:,.2f}** | {lower if isinstance(lower, str) else f'{lower:,.2f}'} | {upper if isinstance(upper, str) else f'{upper:,.2f}'} |")
return '\n'.join(lines) |