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http://dx.doi.org/10.13067/JKIECS.2014.9.1.131

A Study for Sales and Demand Forecasting Model Using Wavelet Neural Networks  

Lee, Jae-Hyun (동명대학교 자율전공학부)
Publication Information
The Journal of the Korea institute of electronic communication sciences / v.9, no.1, 2014 , pp. 131-136 More about this Journal
Abstract
In this paper, we develop a fashion products demand forecasting algorithm using ARIMA model and Wavelet Neural Networks model. To show effectiveness of the proposed method, we analyzed characteristics of time-series data collected in "H" company during 2008-2012 and then performed the proposed method through various analyses. As noted in experimental results, the performance of three types model such as ARIMA, Wavelet Neural Networks and ARIMA + Wavelet Neural Networks show 5.179%, 4.553%, and 4.448.% with respect to MAPE(Mean Absolute Percentage Error), respectively. Thus, it is noted that the proposed method can be used to predict fashion products demand for efficient of operation.
Keywords
ARIMA Model; Wavelet Neural Networks; Demand Forecasting; Fashion Products;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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