Time series decomposition is a statistical method used to separate a time series into its individual components, typically trend, seasonality, and residuals. This technique helps analysts understand underlying patterns and make more accurate forecasts by isolating the effects of different factors on the data over time. By breaking down the time series, it becomes easier to assess the forecast accuracy measures that can be applied to improve predictions.
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