Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/42989
Title: A hybrid SARIMA wavelet transform method for sales forecasting
Authors: Choi, TM 
Yu, Y
Au, KF
Keywords: Sales forecasting
SARIMA model
Wavelet transform
Decision support system
Issue Date: 2011
Publisher: Elsevier
Source: Decision support systems, 2011, v. 51, no. 1, p. 130-140 How to cite?
Journal: Decision support systems 
Abstract: 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.
URI: http://hdl.handle.net/10397/42989
ISSN: 0167-9236
EISSN: 1873-5797
DOI: 10.1016/j.dss.2010.12.002
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