Model Description Table

The model description table contains an entry for each estimated model and includes both a model identifier and the model type. The model identifier consists of the name (or label) of the associated dependent variable and a system-assigned name. In the current example, the dependent variable is Sales of Men's Clothing and the system-assigned name is Model_1.

The Time Series Modeler supports both exponential smoothing and ARIMA models. Exponential smoothing model types are listed by their commonly used names such as Holt and Winters' Additive. ARIMA model types are listed using the standard notation of ARIMA(p,d,q)(P,D,Q), where p is the order of autoregression, d is the order of differencing (or integration), and q is the order of moving-average, and (P,D,Q) are their seasonal counterparts.

The Expert Modeler has determined that sales of men's clothing is best described by a seasonal ARIMA model with one order of differencing. The seasonal nature of the model accounts for the seasonal peaks that we saw in the series plot, and the single order of differencing reflects the upward trend that was evident in the data.

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