Seasonal Decomposition of Time Series is a statistical method used to analyze time series data by breaking it down into its main components: trend, seasonality, and residuals. The trend represents the long-term progression of the data, seasonality captures regular patterns that repeat over specific intervals, and residuals account for random noise or irregular fluctuations. This technique helps in understanding underlying patterns and making forecasts. By isolating these components, analysts can better interpret the data and identify significant changes or anomalies. Tools like Python libraries such as statsmodels facilitate this decomposition process, making it accessible for various applications in fields like economics and environmental science.