A multivariate intelligent decision-making model for retail sales forecasting

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A multivariate intelligent decision-making model for retail sales forecasting
Abstract: A sales forecasting problem in the retail industry is addressed based on early sales. An effective multivariate intelligent decision-making (MID)model is developed to provide effective forecasts for this problemby integrating a data preparation and preprocessing module, a harmony search-wrapper-based variable selection (HWVS) module and a multivariate intelligent forecaster (MIF) module. The HWVS module selects out the optimal input variable subset fromgiven candidate inputs as the inputs of MIF. The MIF is established tomodel the relationship between the selected input variables and the sales volumes of retail products, and then utilized to forecast the sales volumes of retail products. Extensive experiments were conducted to validate the proposed MID model in terms of extensive typical sales datasets from real-world retail industry. Experimental results show that it is statistically significant that the proposed MID model can generate much better forecasts than extreme learning machine-based model and generalized linear model do.
Keywords:	Retail industry Early sales Sales forecasting Multivariate forecasting
Author(s):	.
Source:	Decision Support Systems 55 (2013) 247–255
Subject:	بازاریابی
Category:	مقاله مجله
Release Date:	2013
No of Pages:	9
Price(Tomans):	0
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