Using Box-Jenkins techniques in sales forecasting

This study analyzes the form, stability, and accuracy of Box-Jenkins forecasting models developed for 27 sales series. The order of autoregressive, differencing, and moving average factors is shown for each complete model along with “goodness of fit” criteria. Forecasting models are then presented for a reduced data set and accuracy is compared with seasonally adjusted linear regressions. The results suggest that Box-Jenkins models are often unstable, “goodness of fit” criteria are a poor guide to the best forecasting models, log transforms do not improve accuracy, and Box-Jenkins forecasts are usually (but not always) better than projections made with linear regression techniques.