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

Select the Advanced 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).
Centered moving averages (for even Seasonal lag only) As the first step in the decomposition, a moving average is computed for the series, with the moving average window width equal to the length of one season. If the length of the season is even, then you can choose to use either equal weights for the moving average or unequal weights can be used, where the first and last observation in the moving average window are averaged; this latter method is used when the Centered moving averages check box is selected. If the length of the season is odd, the setting of this check box will not affect the computations.
On OK append components to active work area Select the option boxes in the On OK append components to active work area group box to determine which components will be added to the active work area. If you choose to append components, make sure that there is sufficient "space" in the active work area for the respective variable, that is, that there are at least as many (not locked) backups available as there are components to be saved. Increase the Number of backups parameter if necessary. Also, be sure to Lock all important backups of the original variable (those that you want to retain); otherwise, those backups may be overwritten (refer to the section describing the memory management in the active work area). To lock a backup, double-click in the Lock column on the respective variable.
Moving averages First a moving average is computed for the series, then first a moving average is computed for the series, with the moving average window width equal to the length of one season (as specified in the Seasonal lag box, see above). If the length of the season is even, then you can choose to use either equal weights for the moving average, or unequal weights can be used, where the first and last observation in the moving average window are averaged (select the Centered moving averages check box, see above).
Ratios/Differences In the moving average series (see above), all seasonal (within-season) variability will be eliminated; thus, the differences (in the additive model) or ratios (in the multiplicative model) of the observed and smoothed series will isolate the seasonal component (plus irregular component). Specifically, the moving average is subtracted from the observed series (for the additive model) or the observed series is divided by the moving average values (for the multiplicative model).
Seasonal factors The seasonal factor is then computed as the average (for additive models) or medial average (for multiplicative models) for each point in the season. (The medial average of a set of values is the mean after the smallest and largest values are excluded). The resulting values represent the (average) seasonal component of the series.
Seasonally adj. series. The original series can be adjusted by subtracting from it (additive models) or dividing it by (multiplicative models) the seasonal component. The resulting series is the seasonally adjusted series (i.e., the seasonal component will be removed).
Smoothed trend cycle The cyclical component is different from the seasonal component in that it is usually longer than one season, and different cycles can be of different lengths. The combined trend and cyclical component can be approximated by applying to the seasonally adjusted series a 5 point (centered) weighed moving average smoothing transformation with the weights of 1, 2, 3, 2, 1.
Irregular component Finally, the random or irregular (error) component can be isolated by subtracting from the seasonally adjusted series (additive models) or dividing the adjusted series by (multiplicative models) the trend-cycle component.
Other transformations & plots Click the Other transformations & plots button to display the Transformations of Variables dialog box, which contains options to perform a wide variety of transformations on the data. The transformed series will be appended to the active work area.