Model Attributes (OUTMODELINFO)

This table contains the attributes of the selected model for each time seriesan aggregation of transactional data into specified time intervals and sorted according to unique combinations of the default attributes (BY variables). OUTMODELINFO is also displayed in the results for each modeling node. For more information about the model attributes, see OUTMODELINFO Object.

These statistics are also listed as Model attributes under the attributes on the Data tab and in the Filters pane for the Time Series Viewer and Forecast Viewer. You can use these attributes to create filtersa set of specified criteria that are applied to data in order to identify the subset of data for a subsequent operation, such as continued processing. in all of the viewers. See Creating Filters for more information.

Note: This table is not available for non-time series based modeling nodes such as the Panel Series Neural Network, Stacked Model (NN+TS), and Multistage Model.

This table includes all of the BY variables assigned to the project and the following statistics.

Forecast Attributes from OUTMODELINFO
Variable Name	Variable Label	Description
_MODEL_	Model	the name of the model selected for this time series.
_MODELTYPE_	Model Family	the type of model used (ESM, ARIMA, UCM, or IDM)
_DEPTRANS_	Dependent Variable Transform	the type of dependent variable transformation that was used for this time series.
_SEASONAL_	Seasonal model	indication of seasonal model used for this time series. A value of 1 indicates a seasonal model was used.
_TREND_	Trend Model	indication of trend model used for this time series. A value of 1 indicates a trend model was used.
_INPUTS_	Inputs present	the number of input variables used in the model
_EVENTS_	Events Present	indication of whether events were used in the model. A value of 1 indicates events are present.
_OUTLIERS_	Outliers Present	the number of outliers discovered in the model
_SOURCE_	Model Source	the named source of the model
_STATUS_	Model Status	the execution status of the model. The following status codes can be present. 0 The forecast completed successfully. 3000 Model selection could not be completed. Forecast values are set to missing. 3001 Model selection could not be completed and NOALTLIST prohibits use of default exponential smoothing. Forecast values are set to missing. 3002 The forecast was completed subject to qualification that one or more input variables were omitted from the selected model. This can occur only in the context of ARIMAX or UCM models. 3003 The desired model could not be forecast. The forecast reverted to the default exponential smoothing model. 3004 The attempt to forecast the desired model produced an arithmetic exception. The forecast is generated by CATCH(ESM) processing. 3005 The attempt to forecast the desired model produced an arithmetic exception. The forecast is generated by CATCH(RW) processing. 3006 The attempt to forecast the desired model produced an arithmetic exception. The forecast is generated by CATCH(MISSING) processing. 3007 The mean value forecast is generated as a result of the MINOBS criterion. 3008 There were insufficient nonmissing observations in the variable to be forecast. A missing value forecast is produced. 3009 There were insufficient nonzero observations in the variable to be forecast. A zero-valued forecast is produced.

Last updated: March 16, 2026