STLdekomposition

STLdekomposition, commonly referred to as STL decomposition, is a time-series analysis technique used to partition a series into three components: trend, seasonal, and remainder. Developed to handle complex seasonal patterns, it relies on LOESS (locally estimated scatterplot smoothing) to estimate both seasonal and trend components in a flexible, nonparametric way. The standard model is additive, where the observed value equals the sum of trend, seasonal, and remainder. A multiplicative version can be obtained via log-transforming the data or by applying the method to a standardized series.

The algorithm iteratively applies Loess smoothing: a seasonal component is estimated by smoothing residuals with a moving seasonal window; the trend is then estimated from the remainder after removing the seasonal part; the seasonal component is updated, and after convergence the remainder is computed as the residual of the observed data minus trend and seasonal components. A robust option downweights outliers, increasing resilience to anomalies.

STLdekomposition is appreciated for its ability to handle changing seasonality, irregular patterns, and moderate missing values, unlike classical fixed-parameter methods. It does not require a specific parametric form for the seasonal or trend components. It is widely implemented in statistical software, including R and Python's statsmodels, making it accessible for exploratory data analysis and forecasting tasks. When seasonality is clearly multiplicative, analysts often apply STL to log-transformed data or use a multiplicative variant of the procedure.

In practice, STL decomposition supports forecasting and diagnostic work by isolating components, aiding interpretation of seasonal effects and long-term trends in fields such as economics, meteorology, and engineering.