The seasonal adjustment is used in time series analysis to remove a periodic component with the known period from the observed time series. This adjustment is normally performed through the seasonal decomposition of the time series followed by subtraction of the seasonal component from the observed data.
The reason for the seasonal adjustment is that large periodic variations in the time series (explainable by known periodically varying factors) may mask weaker but more important variations, like upward or downward trends.
The “seasonal” here does not necessarily mean the season of the year. Any periodical component with known period may be removed from the time series. For example, the total sales in a supermarket depend on the day of the week. Thus, the periodic component that may be of practical interest has the period 7 days, not a year. Removal of this component from, say, 60 days time series of daily sales revenue, can make the actual time trends clearer.