Seasonal decomposition is a technique used in time series analysis that breaks down data into its individual components: trend, seasonality, and residuals. This process helps to identify underlying patterns in the data, making it easier to analyze and forecast future values. Understanding these components is crucial for accurate modeling, especially when dealing with seasonal fluctuations in data.
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