A hybrid SARIMA wavelet transform method for sales forecasting

Tsan Ming Choi, Yong Yu, Kin Fan Au

Research output: Journal article publicationJournal articleAcademic researchpeer-review

89 Citations (Scopus)


Time series forecasting, as an important tool in many decision support systems, has been extensively studied and applied for sales forecasting over the past few decades. There are many well-established and widely-adopted forecasting methods such as linear extrapolation and SARIMA. However, their performance is far from perfect and it is especially true when the sales pattern is highly volatile. In this paper, we propose a hybrid forecasting scheme which combines the classic SARIMA method and wavelet transform (SW). We compare the performance of SW with (i) pure SARIMA, (ii) a forecasting scheme based on linear extrapolation with seasonal adjustment (CSD + LESA), and (iii) evolutionary neural networks (ENN). We illustrate the significance of SW and establish the conditions that SW outperforms pure SARIMA and CSD + LESA. We further study the time series features which influence the forecasting accuracy, and we propose a method for conducting sales forecasting based on the features of the given sales time series. Experiments are conducted by using real sales data, hypothetical data, and publicly available data sets. We believe that the proposed hybrid method is highly applicable for forecasting sales in the industry.
Original languageEnglish
Pages (from-to)130-140
Number of pages11
JournalDecision Support Systems
Issue number1
Publication statusPublished - 1 Apr 2011


  • Decision support system
  • Sales forecasting
  • SARIMA model
  • Wavelet transform

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management


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