Time series decomposition is the process of breaking down a time series data set into its individual components, typically including trend, seasonality, and residuals. This technique helps in understanding the underlying patterns and relationships within the data, making it easier to identify outliers and forecast future values. By separating these components, analysts can gain insights into how different factors contribute to the overall behavior of the time series.
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