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Forecasting brand sales with wavelet decompositions of related causal series

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  • Antonis A. Michis

Abstract

We consider methods for forecasting brand sales utilising wavelet decompositions of related causal series. Wavelet decompositions can uncover the hidden periodicities inherent in marketing time series like pricing and can therefore provide superior information in causal sales forecasting methods. We specifically address the problem of multicollinearity since the proposed wavelet packet transformation of a time series of length T, generates 2T – 2 correlated vectors of coefficients, each of length T. We find that partial least-squares provide the most accurate forecasting method which at the same time achieves the desired dimension reduction in the estimation problem.

Suggested Citation

  • Antonis A. Michis, 2009. "Forecasting brand sales with wavelet decompositions of related causal series," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 1(2), pages 95-110.
    • Handle: RePEc:ids:ijbfmi:v:1:y:2009:i:2:p:95-110
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    Cited by:

    1. I A Eckley & G P Nason, 2018. "A test for the absence of aliasing or local white noise in locally stationary wavelet time series," Biometrika, Biometrika Trust, vol. 105(4), pages 833-848.
    2. Aykroyd, Robert G. & Barber, Stuart & Miller, Luke R., 2016. "Classification of multiple time signals using localized frequency characteristics applied to industrial process monitoring," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 351-362.

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