Quantitative Analysis for Management, 13/e

5.7 Forecasting Models—Trend, Seasonal, and Random Variations

When both trend and seasonal components are present in a time series, the forecasting model selected must address these. The decomposition method, which uses seasonal indices, is a very common approach. Multiple regression models are also common, with dummy variables used to adjust for seasonal variations in an additive time-series model.

The Decomposition Method

The process of isolating linear trend and seasonal factors to develop more accurate forecasts is called decomposition. The first step is to compute seasonal indices for each season as we have done with the Turner Industries data. Then, the data are deseasonalized by dividing each number by its seasonal index, as shown in ...