Auto-forecasting Settings

Task Settings

Set the forecast task

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:

  • Diagnose : performs model selection, estimates parameters of the selected model, and produces forecasts. This is the default.
  • Fit : estimates parameters for the models that you select and then forecasts. No model selection is performed. You must run the Diagnose task successfully before running the Fit task.
  • Forecast : forecasts using model parameter estimates. You must run the Diagnose task successfully before running the Forecast task. After the Diagnose task has been run, if any settings are changed, the Diagnose task must be run again before running the Forecast task.
  • Select : performs model selection from the models that you select, estimates parameters of the selected model, and produces forecasts. You must run the Diagnose task successfully before running the Select task.
  • Update : estimates parameters from the models that you select and then forecasts. No model selection is performed. Update differs from Fit in that the estimated parameters are used as starting values in the estimation. You must run the Diagnose task successfully before running the Update task. After the Diagnose task has been run, if any settings are changed, the Diagnose task must be run again before running the Update task.

Model Generation

You can change the following settings in the Options pane of the pipeline. For more information, see Options Pane.

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 seriesan aggregation of transactional data into specified time intervals and sorted according to unique combinations of the default attributes (BY variables).

Include ESM models

Turn this setting on to include an exponential smoothing model (ESM) for diagnosis.

Include ARIMAX models

Turn this setting on to include an ARIMAX model for diagnosis.

Include IDM models

Turn this setting on to include an intermittent demand model (IDM) for diagnosis.

  • IDM Settings — This section is available when Include IDM models is enabled.
    Sensitivity level for intermittency test

    Specify an integer greater than one. This setting is used to determine whether a time series is intermittent. If the demand interval is equal to or greater than this number, then the series is assumed to be intermittent.

    IDM method

    Select one of the following models:

    • Average : requests the extended sample autocorrelation function.
    • Best : uses the single smoothing model to fit the average demand component.
    • Croston : uses the two smoothing models to fit the demand interval component and the demand size component.
Include UCM models

Turn this setting on to include a UCM model for diagnosis.

Include external models

Turn this setting on to include an external model to use for diagnosis.

  • CASLIB name from an external data source — Enter the name of the caslib where the model is stored.
  • Table name from an external data source — Enter the name of the table. The table name cannot exceed 32 bytes.
Note:
  • If the external model uses a dummy data set, any independent variables declared in the dummy data set must use the same names as the independent variables in the project time series. Otherwise, the external model is not used.
  • If the external model cannot be found in memory or in the file system, a warning is issued and the node execution proceeds.

Model Selection

Number of data points used in the holdout sample

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.

Percentage of data points used in the holdout sample

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.

Model selection criterion

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.

Output Tables

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 tables are optional. 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.

Last updated: March 16, 2026