What is decomposition-based forecasting and how is it used in modern time series systems?

Decomposition-based forecasting splits a time series into trend, seasonality, and residual components before modeling them separately.

Decomposition-based forecasting assumes that a time series can be broken into interpretable components: trend (long-term direction), seasonality (repeating patterns), and residual (noise). Each component is modeled independently using suitable techniques, and then recombined. Modern systems use classical decomposition (like STL) or learned decomposition in deep models to improve interpretability and forecasting accuracy.