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.
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References
Chopra, S., Meindl, P.: Supply Chain Management Strategy, Planning, and Operation. Tsinghua University Press, Beijing (2001)
Wang, Y.J.: Data Mining and Practical Modeling Methods for Supply Chain Management. Tsinghua University Press, Beijing (2001)
Joseph, P.M.: A Review of Selected Recent Advances in Technological Forecasting. Journal of Technological Forecasting & Social Change 70(8), 719–733 (2003)
Kesten, C.G.: Game Theory, Simulated Interaction, and Unaided Judgment for Forecasting Decisions in Conflicts: Further Evidence. Journal of Forecasting, 463–472 (2005)
Geoffrey, A.P., Bernard, J.M.: Twenty-five Years of Progress, Problems, and Conflicting Evidence in Econometric Forecasting. What about the next 25 years? International Journal of Forecasting 22(3), 475–492 (2006)
Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20, 273–297 (1995)
Hansen, L.K., Salamon, P.: Neural Network Ensembles. IEEE Trans. on Pattern Analysis and Machine Intelligence. 12, 993–1001 (1990)
Salgado, R.M., Pereira, J.J.F., Ohishi, T., Ballini, R., Lima, C.A.M., Zuben, F.J.V.: A Hybrid Ensemble Model Applied to the Short-term Load Forecasting Problem. In: International Joint Conference on Neural Networks, pp. 2627–2634 (2006)
Lean, Y.U., Wang, S.Y., Kin, K.L.: Forecasting China’s Foreign Trade Volume With a Kernel-based Hybrid Econometrical Ensemble Learning Approach. Journal of Systems Science and Complexity 28, 1–19 (2007)
Liu, Y., Yin, Y.F., Gao, J.J., Tan, C.L.: Demand Forecasting by Using Support Vector Machine. In: The Third International Conference on Natural Computation (ICNC 2007), pp. 272–276 (2007)
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Liu, Y., Wei, W., Wang, K., Liao, Z., Gao, Jj. (2011). Balanced-Sampling-Based Heterogeneous SVR Ensemble for Business Demand Forecasting. In: Huang, DS., Gan, Y., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing. ICIC 2011. Lecture Notes in Computer Science, vol 6838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24728-6_13
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DOI: https://doi.org/10.1007/978-3-642-24728-6_13
Publisher Name: Springer, Berlin, Heidelberg
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