Smoothing Transformations

Creates various smoothing transformations for time series data. Available methods include moving average and moving median smoothing, simple exponential smoothing (see also the designated seasonal and nonseasonal exponential smoothing facilities), and the 4253H filter.

General

Element Name Description
Type of transformation Select the desired type of smoothing transformation.
N points Specifies the width of the moving window for N point moving average and moving median transformations; for other transformations, this option is ignored. For example, if you select the N-points moving average option, each point in the transformed series is computed as the mean of N points, the so-called moving average window. If the N parameter is odd, then the moving average is naturally centered in the middle of the moving average window. If N is even, the moving average is centered by averaging each pair of uncentered means.
Averg/medians from prior obs If this option is selected, the moving averages or medians are computed from the N preceding values for each value; if this option is not selected, the moving average/median window will be centered over each respective observation. For other transformations, this option is ignored.
Lambda for exp. smoothing Specifies the value of lambda for simple exponential smoothing; for other transformations, this option is ignored.
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.

Autocorrelations and Plots

Element Name Description
Labels plots with case names Labels the horizontal axis in plots with case numbers or case names (if available).
Creates autocorrelations Creates the autocorrelation function for the differenced series.
Number of lags Enter a value in the Number of lags box to determine the maximum number of lags for which the autocorrelations are to be computed.

Missing Data

Element Name Description
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.