Decomposition might refer to Time Series Decomposition. Decomposing a time-series into Seasonal and Trend components can be achieved by using several methods, such as STL (which uses a Loess smoothing algorithm).
Prefer using stl-decomposition if applicable.
Time series data can exhibit a variety of patterns, and it is often helpful to split a time series into several components, each representing an underlying pattern category.
There are three types of time series patterns: trend, seasonality and cycles. When we decompose a time series into components, we usually combine the trend and cycle into a single trend-cycle component (often just called the trend for simplicity). Thus we can think of a time series as comprising three components: a trend-cycle component, a seasonal component, and a remainder component (containing anything else in the time series). For some time series (e.g., those that are observed at least daily), there can be more than one seasonal component, corresponding to the different seasonal periods.
