Calendar and seasonal adjustment

A time series is a sequence of observations collected at regular time intervals, for example, a monthly time series. It characterises indicator changes or development thereof.  Seasonality and calendar effects are present in a large number of economic time series.

Seasonality or seasonal fluctuations of time series mean those movements, which recur with similar intensity in the same season each year. For example, each year Christmas shopping time can be observed in time series reflecting retail sales statistics. Change of seasons, social habits and influence of institutional factors are among the main causes of seasonality.

The calendar effects cover influence of calendar on time series. It is impact left by differing number of working days (or Mondays, Tuesdays and other days of the week) in months on changes of indicator. For example, number of working days differing among the months may affect goods produced time series.

When the time series are influenced by seasonality or calendar effects, it may be difficult to get clear understanding on indicator changes over the time.  Seasonal adjustment is made to eliminate seasonal fluctuations and calendar effects in time series.

As a result seasonally adjusted time series, from which seasonality and calendar effects have been removed, are produced. It means that seasonally adjusted time series provide an estimate for what is “new” in the series, for example turning points in trends, business cycle or irregular component.  Moreover seasonal adjustment results in calendar adjusted time series, in which calendar effects or varying number of working days in months has been eliminated. Specifics of seasonally adjusted statistics allows improving data comparability over time:

  • Seasonally adjusted time series do not contain seasonal fluctuations and calendar effects, thus it is possible to compare, for example, data on the current month with the previous month's data.
  • Calendar adjusted time series are not influenced by calendar effects and are used to compare, for example, statistics on current month with the data on corresponding month of the previous year.

The seasonal adjustment is made taking into account seasonal adjustment guidelines developed by the European Statistical System.