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The broad aim of studying time series of economic data is the prompt recognition of significant changes in the direction and level of economic activity.
Many time series have a recurring seasonal pattern that obscures the underlying behaviour of the series. Seasonal adjustment is the process of estimating and removing the varying seasonal effects from a time series in order to reveal non-seasonal features. Examples of seasonal effects include sales increases at Christmas and annual cycles in agricultural production. Sometimes during the seasonal adjustment process we also estimate and remove calendar effects.
The resulting seasonally adjusted series enables comparison of data for adjacent months/quarters.
Example of a seasonal time series: