Automatic Method Selection

Multiple forecasting methods are valuable if the appropriate model can be selected in an accurate and efficient manner. Oracle Retail has developed different meta-methods that can automatically select the best method among competing models. These models include level information, level and trend information, and level, trend, and seasonality information. The optimal smoothing parameters for each model form are determined automatically (that is, greater smoothing is applied to noisier data). The final selection between the resulting models is made according to a performance criterion that involves a trade-off between the model's fit over the historic data and its complexity. The amount of available historic information can affect the complexity of the model. Automatic Exponential Smoothing (Auto ES) and Seasonal Exponential Smoothing (Seasonal ES) chooses between competing models according to performance criterion, looking at the tradeoff between history and its complexity.