Seasonal Decomposition of Time Series (STL)

Seasonal and Trend decomposition using Loess (STL) is a method that separates a time series into three components: trend (long-term movement), seasonality (regular patterns repeating over time), and residuals (irregular or random parts). It uses a flexible technique called Loess smoothing to identify and extract these patterns, even when seasonality varies over time. This helps analysts better understand underlying behaviors, forecast future values, and identify anomalies. STL is valued for its adaptability and robustness in handling complex, real-world data with changing seasonal effects.