Balanced-Sampling-Based heterogeneous SVR ensemble for business demand forecasting

Authors:
Yue Liu;Wang Wei;Kang Wang;Zhenjiang Liao;Jun-jun Gao
Affiliations:
School of Computer Engineering & Science, Shanghai University, Shanghai, China;School of Computer Engineering & Science, Shanghai University, Shanghai, China;School of Computer Engineering & Science, Shanghai University, Shanghai, China;School of Computer Engineering & Science, Shanghai University, Shanghai, China;Sydney Institute of Language and Commerce, Shanghai University, Shanghai, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Year:
2011

Citing 3
Cited 0

Support-Vector Networks

Machine Learning
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Demand Forecasting by Using Support Vector Machine

ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03

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Abstract

An accurate demand forecasting model has academic and practical significance to supply chain management. However, multi-source data and error data have great effect on the demand prediction accuracy. Therefore, a balanced-sampling-based ensemble of heterogeneous support vector regression forecasting method named BS-EnHSVR (Balanced-Sampling-based Ensemble of Heterogeneous SVR) is proposed in this paper to improve the prediction accuracy by employing balanced sampling and heterogeneous ensemble learning techniques. Training dataset is firstly classified to different clusters by using clustering algorithm, and then sample data from each cluster equally to generate training subset for training different individual SVR models with different training parameters for ensemble. Experimental results on beer sales show that the proposed method has good usability and generalization ability.