DocumentCode :
120288
Title :
A Novel Forecasting Method for Large-Scale Sales Prediction Using Extreme Learning Machine
Author :
Ming Gao ; Wei Xu ; Hongjiao Fu ; Mingming Wang ; Xun Liang
Author_Institution :
Sch. of Inf., Renmin Univ. of China, Beijing, China
fYear :
2014
fDate :
4-6 July 2014
Firstpage :
602
Lastpage :
606
Abstract :
With the rise of e-commerce business, sales forecasting plays an increasingly important role, for accurate and speedy forecasting can help e-commerce companies solve all the uncertainty associated with demand and supply and reduce inventory cost. As the rapid growth in the amount of data, traditional intelligence models like Neural Networks have weakness in terms of speed. In this paper, we introduce the algorithm of ELM (extreme learning machine). In addition, we subjoin many e-commerce related indicators to increase the accuracy and reliability of prediction. In sum, the new model provides a better result both in terms of speed and accuracy. Experiments are conducted with the real sales data from an e-commerce company in China.
Keywords :
electronic commerce; forecasting theory; learning (artificial intelligence); retail data processing; sales management; China e-commerce company; demand and supply; e-commerce business; extreme learning machine; forecasting method; intelligence models; inventory cost reduction; large-scale sales prediction; sales forecasting; Accuracy; Books; Companies; Educational institutions; Forecasting; Predictive models; Training; E-commerce; ELM Algorithm; Sales prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Sciences and Optimization (CSO), 2014 Seventh International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-5371-4
Type :
conf
DOI :
10.1109/CSO.2014.116
Filename :
6923757
Link To Document :
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