Seasonal decomposition is a technique used to break down a time series into its constituent components: trend, seasonal, and residual (or irregular) components. This method helps in isolating the seasonal effect, which could be due to various factors like climate, social behavior, or environmental conditions, making it easier to understand the underlying structure of the time series data.
