Seasonal decomposition is a statistical method used to analyze time series data by breaking it down into its fundamental components: trend, seasonality, and residuals. The trend represents the long-term progression of the data, while seasonality captures regular patterns that occur at specific intervals, such as daily, monthly, or yearly. The residuals are the random fluctuations that remain after removing the trend and seasonal effects. This technique is particularly useful in fields like economics, meteorology, and sales forecasting, where understanding patterns over time is crucial. By applying seasonal decomposition, analysts can better predict future values and make informed decisions based on historical data trends and seasonal variations.