Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data

R. J. Kuo (), Y. S. Tseng and Zhen-Yao Chen
Additional contact information
R. J. Kuo: National Taiwan University of Science and Technology
Y. S. Tseng: American Megatrends Inc.
Zhen-Yao Chen: DE LIN Institute of Technology

Journal of Intelligent Manufacturing, 2016, vol. 27, issue 6, No 4, 1207 pages

Abstract: Abstract Sales forecasting plays a very important role in business operation. Many researches generally employ statistical methods, such as regression or auto-regressive integrated moving average model, to forecast the product sales. However, they only can consider the quantitative data. Some exogenous qualitative variables have more influence on forecasting result. Thus, this study attempts to propose a integrated forecasting system which is able to consider both quantitative and qualitative factors to achieve a more comprehensive result. Basically, fuzzy neural network is first employed to capture the expert knowledge regarding some qualitative factors. Then, it is combined with the time series data using an artificial immune system based back-propagation neural network. A laptop sales data set provided by a distributor in Taiwan is applied to verify the proposed approach. The computational result indicates that the proposed approach is superior to other forecasting methods. It can be used to decrease the inventory costs and enhance the customer satisfaction.

Keywords: Back-propagation neural network; Artificial immune system; Fuzzy neural network; Sales forecasting; Evolutionary algorithm (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10845-014-0944-1

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