Example Concept: Seasonal decomposition involves splitting a time series into trend, seasonal, and residual components using methods like STL (Seasonal-Trend decomposition using LOESS) or classical decomposition. By isolating these components, you can model the trend and seasonality separately, often using techniques like ARIMA or exponential smoothing, and then combine them to produce a more accurate forecast. This method helps in identifying and adjusting for seasonal patterns, leading to improved forecast accuracy.