Regular seasonal fluctuations are those movements which, on the basis of the past movements of the time series in question, can under normal circumstances be expected to recur with similar intensity in the same season each year. Calendar effects include effects caused by the number of working days or calendar days in the month, or the dates of particular occasions, such as Easter, within the year – the influence of such effects on a particular month can vary from year to year, but they can be quantified and adjusted.
An example of a seasonal series is the flow of credit card lending to individuals, where credit card lending tends to increase in December as consumers spend more in the run-up to Christmas. The non seasonally adjusted flows for credit card lending to individuals (red line below) show a seasonal peak in December followed by a seasonal trough in January. These can be explained by more spending in December and lower spending in January.
Data users are often interested in series which have been adjusted to remove seasonal effects, since these may give a better indication of the underlying movements. For those series where identifiable seasonality is detected, a seasonally adjusted version is also published. The seasonally adjusted flows for credit card lending to individuals (blue line below), removes both the regular seasonal movements and any calendar effects.