Seasonal Decomposition (Census I)

Performs classical seasonal decomposition (Census method I; ratio-to-moving averages method); computes moving averages, ratios or differences, seasonal factors, seasonally adjusted series, the smoothed trend-cycle component, and the irregular component.

Element Name Description
General
Detail of computed results reported Specifies the detail of computed results reported; if Minimal detail is requested, only a spreadsheet with all components (results of the seasonal decomposition) will be reported; if Comprehensive detail is requested, plots of the different components are produced; if All results is requested, histograms and normal probability plots of the irregular (error) series are also reported, along with a summary line graph.
Seasonal model Specifies the seasonal model for the computations.
Seasonal lag Specifies the length of one seasonal cycle. The default value is 12 (e.g., 12 months in each year).
Centered moving averages Use centered moving averages.

 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, 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 option is selected. If the length of the season is odd, the setting of this option will not affect the computations.
Labels plots with case names Labels the horizontal axis in plots with case numbers or case names (if available).
Generates data source, if N for input less than Generates a data source for further analyses with other Data Miner nodes if the input data source has fewer than k observations, as specified in this edit field; note that parameter k (number of observations) will be evaluated against the number of observations in the input data source, not the number of valid or selected observations.
Missing Data
Replace missing data Specifies how missing data is to be replaced. Missing data can be replaced by the overall mean, interpolated from adjacent points, replaced by the mean or median of N adjacent points (on both sides of the hole), or estimated (predicted) from linear trend regression.

 Note that as long as the missing data are at the end of the series (trailing missing data) or the beginning of the series (leading missing data), the missing data will simply be ignored.
Number of adjacent points Applicable if missing data are replaced by the mean or median of N adjacent points; specify N.

 The missing data are replaced by the mean or median computed from the N adjacent points on both sides of the hole of missing data.