Time Series Decomposition for Financial Forecasting

Design an advanced time series decomposition methodology for financial forecasting that can handle complex, non-stationary financial data streams. Develop a hybrid approach combining classical decomposition techniques with machine learning models to extract trend, seasonal, and residual components with high precision. Create a flexible framework that can adapt to different financial time series characteristics, including high-frequency trading data, economic indicators, and asset price movements.