Advanced Time Series Forecasting Ensemble Methodology

Construct a sophisticated time series forecasting framework that combines multiple advanced techniques including ARIMA, Prophet, LSTM neural networks, and state-of-the-art ensemble methods. Develop a flexible architecture that can automatically select and weight different forecasting models based on historical performance, handle complex seasonality patterns, and provide comprehensive uncertainty estimates. Include detailed approaches for feature engineering, model interpretability, and handling external intervention effects.