Benchmarking Forecasting Models

BENCHMARKING FORECASTING MODELS By Chaman L. Jain, St. John’s University The selection of a model plays a very important role in forecasting. Each dataset has a pattern of its own. Each model captures a specific data pattern. So for best results, it is important to use the right model for the right dataset. This raises a question of how to select the best model. To answer, one needs to know what kinds of models are available and which model is most suitable and where. Also, one needs to know some basics about models and modeling. There are basically three types of forecasting models: (1) Time Series, (2) Cause-and-Effect, and (3) Judgmental. TIME SERIES MODELS In Time Series modeling, we extrapolate the past data using one method or the other in search of the best statistical fit. Each Time Series model assumes that the past pattern will continue into the future. One of the Time Series models is the Percent-Change method, which assumes that the average percentage increase/decrease in sales experienced in the past will continue into the future. If sales in the past increased on the average by 5%, then the forecast of the next month will be the sales of current period plus ...