Ratios-to-Moving Averages Classical Seasonal Decomposition (Census Method I) - Quick Tab

Select the Quick tab of the Ratios-To-Moving Averages Classical Seasonal Decomposition (Census Method I) dialog box to access the options described here.

Element Name Description
Seasonal model Use the options in the Seasonal model group box to determine the seasonal model. In general, the seasonality in the series can be Additive or Multiplicative. For example, when tracking the sales for a toy, it can be expected that during the December holiday season sales will increase; another seasonal increase could possible occur during the summer vacation months.
Additive/Multiplicative The seasonal fluctuation (e.g., the increase in sales during December) can be additive (e.g., on average, sales increase in December by about 1 million dollars over the year's average), or it can be multiplicative (on average, sales increase in December by 30%, that is, by a factor of 1.3). In plots of a series, the distinguishing characteristic between these two types of seasonal components is that in the Additive case, the series shows steady seasonal fluctuations, regardless of the overall level of the series; in the Multiplicative case, the size of the seasonal fluctuations vary, depending on the overall level of the series. Refer to Seasonal decomposition (Census I) for additional details.
Seasonal lag Enter a value in the Seasonal lag box to determine the assumed length of one seasonal cycle. The default value is 12 (e.g., 12 months in each year). When changed, this parameter is retained (remembered) for other time series analyses involving a seasonal component (e.g., in ARIMA models or in Exponential smoothing). See the Advanced tab for options regarding centered moving averages.