Seasonal adjustment is the process of estimating and removing seasonal effects from a time series in order to better reveal certain non-seasonal features. An example of a seasonal effect is an increase in accounting services during the tax season. (Seasonal effects are defined more precisely below.)

Seasonal movements are often large enough that they mask other characteristics of the data that are of interest to analysts who track current economic trends. For example, if each quarter has a different seasonal tendency toward high or low values it can be difficult to detect the general direction of a time series' recent quarterly movement (increase, decrease, turning point, no change, consistency with another economic indicator, etc.). Seasonal adjustment produces data in which the values of neighboring quarters are usually easier to compare. Many data users prefer seasonally adjusted data because they want to see those characteristics that seasonal movements tend to mask, especially changes in the direction of the series.

Seasonal adjustment procedures remove quarterly time series estimate effects that occur in the same calendar quarter with similar magnitude and direction from year to year. Examples of these effects include economic activity tied to the tax season or to the travel season. The seasonal factors are estimates of average effects for each quarter. It is important to note that seasonal factors are based on present and past experience and that future data may show a different pattern of seasonal factors.

Every not seasonally adjusted series published in the Quarterly Services Report is tested for seasonality using X-13ARIMA-SEATS. Series which test positively for seasonality, i.e., show indications of seasonal behavior, are identified as potentially eligible for seasonal adjustment. Diagnostics used to determine which series to seasonally adjust can be found here.

We use X-13ARIMA-SEATS to derive our seasonal adjustment and produce seasonal factors. X-13ARIMA-SEATS is a seasonal adjustment program developed at the U.S. Census Bureau in collaboration with the Bank of Spain that provides both X-11 method seasonal adjustments and ARIMA model-based seasonal adjustments and diagnostics.

QSS estimates are adjusted using the X-11 filter-based adjustment procedure. QSS is currently using version 1.1, build 61 of X-13ARIMA-SEATS.

Seasonally adjusted revenue is published on table 1a of the Quarterly Services Report. Seasonally adjusted expenses are published on table 1b of the Quarterly Services Report. Additionally, seasonally adjusted data is available on the Census Bureau's time series database.

Seasonally adjusted estimates are provided for all quarters for which there are unadjusted estimates available. Full time series are accessible here. The quarterly press release contains seasonally adjusted estimates for the most recent six quarters