|Forecasting Process Details|
This section explains the goodness-of-fit statistics reported to measure how well different models fit the data. The statistics of fit for the various forecasting models can be viewed or stored in a data set by using the Model Viewer window.
Statistics of fit are computed by using the actual and forecasted values for observations in the period of evaluation. One-step forecasted values are used whenever possible, including the case when a hold-out sample contains no missing values. If a one-step forecast for an observation cannot be computed due to missing values for previous series observations, a multi-step forecast is computed, using the minimum number of steps as the previous nonmissing values in the data range permit.
The various statistics of fit reported are as follows. In these formulas, n is the number of nonmissing observations and k is the number of fitted parameters in the model.
Number of Missing Actuals.
The number of missing actual values.
Number of Missing Predicted Values.
The number of missing predicted values.
Number of Model Parameters.
The number of parameters fit to the data. For combined forecast, this is the number of forecast components.
The largest prediction error.
The smallest prediction error.
Maximum Percent Error.
The largest percent prediction error, . The summation ignores observations where .
Minimum Percent Error.
The smallest percent prediction error, . The summation ignores observations where .
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