You can change the following settings in the Options pane of the pipeline. For more information, see Options Pane.
Specify the task to run for this modeling node. These tasks must be run sequentially. For example, the Diagnose task can be run independently, but the Fit task requires that you run the Diagnose task first.
Choose from one of the following tasks:
Specify a minimum value that a time seriesan aggregation of transactional data into specified time intervals and sorted according to unique combinations of the default attributes (BY variables) must meet to be fit using the models in the selection list. Time series that do not meet this minimum value are forecasta numerical prediction of a future value for a specified time period for each unique combination of BY variable values as the mean of the observations in the series. This value must be greater than or equal to one. The default value is 2.
Specify that a trend model is not fitted to any series with fewer nonmissing observations than the value specified. The value must be an integer that is greater than or equal to 1. The default value is 2. Trend models are not included for any series with fewer nonmissing observations than this value.
Incorporation of a trend is checked only for smoothing, UCM, and ARIMA models. For the smoothing case, only simple smoothing is not a trend model. For UCM, the absence of a slope component qualifies it as not a trend model. For ARIMA, there must be no differencing of the dependent variable for it to be considered as not a trend model.
Seasonal models are not included for any series with fewer nonmissing observations than this value, multiplied by the seasonal length. Specify an integer greater than or equal to one.
For best results, make sure at least two of these models are selected. If none of the models are selected, an ESM model (ESM BEST) is used for all time series.
Turn this setting on to include an exponential smoothing model (ESM) for diagnosis.
Turn this setting on to include an ARIMAX model for diagnosis.
Turn this setting on to include an intermittent demand model (IDM) for diagnosis.
Turn this setting on to include a UCM model for diagnosis.
Select
Yes
if you want to combine the selected models for diagnosis.
Enter a positive integer to be used as the size of the holdout samplethe number of periods of the most recent data that should be excluded from the parameter estimation. The holdout sample can be used to evaluate the forecasting performance of a candidate model.. The actual holdout sample is the minimum between this value and the Percentage of data points used in the holdout sample . The default value is zero, which means no holdout sample is used.
Enter a value between 0 and 100 to specify the percentage of the sample that is used for the holdout sample. The actual holdout sample is the minimum between this value and the Number of data points used in the holdout sample . This option is displayed only if Number of data points used in the holdout sample is greater than zero.
Choose the statistics of fit to use for selecting the best model in this modeling node. For descriptions for each option, see Descriptions of Model Selection Criteria.
Setting the reconciliation level overrides the default setting selected when assigning the BY variable roles for the project on the Data tab. For more information, see Assigning the Default Attributes.
The reconciliation level is specified for this node by selecting a BY variable from the hierarchy. Consider the order of the default attributes (BY variables) defined on the Data tab. Select a variable to determine the type of reconciliation methodthe method that specifies the level in the hierarchy where the process of reconciliation starts. The following reconciliation methods are available: bottom-up method, middle-out method, and top-down method. .
By default, the reconciliation level set on the Data tab for the BY variables is used.
For a description of top-down, middle-out, and bottom-up reconciliation methods, see Understanding Hierarchy Reconciliation.
The following tables are automatically generated when running this modeling node. The tables can be saved to another caslib by using the Save Data Node.
The following optional tables can be selected for generation. Select any tables that you want generated when this node is run. After the node is run, the tables can be saved using the Save Data Node.