Exponential Smoothing

Performs simple and complex (multiple-parameter) exponential smoothing; models can include additive and multiplicative seasonal components, and linear, exponential, and damped trends components.

Element Name Description
Model
Seasonal component Specifies whether to include a seasonal component in the analysis.
Seasonal lag Specifies the length of one seasonal cycle if the current analysis is to include a seasonal component.
Trend component Specifies whether and what type of trend component to include in the analysis.
Parameter alpha Specifies the constant (non-seasonal, non-trend) smoothing parameter Alpha necessary for all models.
Parameter delta Specifies the seasonal smoothing parameter Delta; applicable only to analyses that include a seasonal component.
Parameter gamma Specifies trend smoothing parameter Gamma; applicable to linear and exponential trend models, and for damped trend models without seasonality.
Parameter phi Specifies trend smoothing parameter phi; applicable to damped trend models.
Number of cases to forecast Specifies the number of cases to forecast.
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.
Summary Plots
Summary plot Creates and reports a summary plot for the different components.
Labels plots with case names Labels the horizontal axis in plots (if Level of detail is All results) with case numbers or case names (if available).
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.