The Rebuild/reestimate forecaster on incoming data check box allows the user to specify that the forecaster should be trained on the incoming data rather than primed. This allows the forecasting model to be brought up to date with the latest historical data. After training is complete, a forecast is generated as described above. Selecting this option enables the Save forecaster field. This field can be used to specify a file to which the updated forecasting model will be saved out to. Leaving the field blank tells the step not to save the updated forecasting model.
4.2 Checking Data Fields
The Fields mapping tab allows the user to check how the step is mapping incoming transformation fields to those that the model saw in its training data. The step matches both field names and types - note that this is done between incoming Kettle fields and the original training data fields (before any internal transformations done by the forecasting model itself). Any training data fields that don't have counterpart in the incoming data are indicated by an entry labelled "missing". If there is a difference in type between a training field and an incoming field, then this will be indicated by the label "type-mismatch". In both cases, the forecaster will receive a missing value as input for the field in question for all incoming data rows. This will impact forecasting performance to a greater or lesser degree.