A wavelet smoothing method to improve conditional sales forecasting

This article proposes a wavelet smoothing method to improve conditional forecasts generated from linear regression sales response models. The method is applied to the forecasted values of the predictors to remove forecast errors and thereby improve the overall forecasting performance of the models. Eight empirical studies are presented in which the purpose was to forecast detergent sales in the Netherlands, and wavelet smoothing was compared with a moving average and a band‐pass filter. All methods were found to improve forecasts. Wavelet smoothing provided the best results when applied on highly volatile marketing time series. In contrast, it was less effective when applied on highly aggregated and smooth time series. An advantage of wavelets is that they are flexible enough to allow for data characteristics like abrupt changes, spikes and cyclical changes that are usually associated with price changes and promotions.