Model Statistics Table

The model statistics table provides summary information and goodness-of-fit statistics for each estimated model. Results for each model are labeled with the model identifier provided in the model description table. First, notice that the model contains two predictors out of the five candidate predictors that you originally specified. So it appears that the Expert Modeler has identified two independent variables that may prove useful for forecasting.

Although the Time Series Modeler offers a number of different goodness-of-fit statistics, we opted only for the stationary R-squared value. This statistic provides an estimate of the proportion of the total variation in the series that is explained by the model and is preferable to ordinary R-squared when there is a trend or seasonal pattern, as is the case here. Larger values of stationary R-squared (up to a maximum value of 1) indicate better fit. A value of 0.948 means that the model does an excellent job of explaining the observed variation in the series.

The Ljung-Box statistic, also known as the modified Box-Pierce statistic, provides an indication of whether the model is correctly specified. A significance value less than 0.05 implies that there is structure in the observed series which is not accounted for by the model. The value of 0.984 shown here is not significant, so we can be confident that the model is correctly specified.

The Expert Modeler detected nine points that were considered to be outliers. Each of these points has been modeled appropriately, so there is no need for you to remove them from the series.

Next