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A wavelet smoothing method to improve conditional sales forecasting

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  • Antonis A Michis

    (Central Bank of Cyprus, Nicosia, Cyprus)

Abstract

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.

Suggested Citation

  • Antonis A Michis, 2015. "A wavelet smoothing method to improve conditional sales forecasting," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(5), pages 832-844, May.
    • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:5:p:832-844
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    Citations

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    Cited by:

    1. Antonis A. Michis & Guy P. Nason, 2015. "Estimation and Prediction of Shipping Trends with the Data-Driven Haar-Fisz Transform," Working Papers 2015-1, Central Bank of Cyprus.
    2. Antonis A. Michis & Guy P. Nason, 2017. "Case study: shipping trend estimation and prediction via multiscale variance stabilisation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(15), pages 2672-2684, November.
    3. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    4. Antonis A. Michis, 2023. "Retail distribution evaluation in brand-level sales response models," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(3), pages 366-378, September.
    5. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    6. Antonis A. Michis, 2021. "Wavelet Multidimensional Scaling Analysis of European Economic Sentiment Indicators," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 443-480, October.
    7. Jian Liu & Chunlin Liu & Lanping Zhang & Yi Xu, 2020. "RETRACTED ARTICLE: Research on sales information prediction system of e-commerce enterprises based on time series model," Information Systems and e-Business Management, Springer, vol. 18(4), pages 823-836, December.

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