By deseasonalisation of data we mean the elimination of seasonal variations from the observed values of a time series. After elimination of the trend values from a time series, this is done specially, with a view to studying the cyclic components and isolating the same from the random variations. The process deseasonalisation helps very much in the decomposition of time series into its various components, viz : trend, seasonal, cyclic and irregular. It also helps us in adjusting a given time series for studying variations and leaves us with the remaining components, viz : trend, cyclical and irregular variations. As such, deseasonalisation of data helps a lot to the businessmen and management executives in planning their production and marketing programme. It, also, helps in the proper interpretation of the observed values of a time series. If the observed values are not adjusted for the seasonal variations, then the seasonal upswings and downswings may be mistaken as the period of prosperity, or depression respectively.
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