Abstract
The application of neural network ensemble (NNE) to economic forecasting can heighten the generalization ability of learning systems through training multiple neural networks and combining their results. An improved principal component analysis (IPCA) is developed to extract the principal component of the economic data under the prerequisite that the main information of original economic data is not lost, and the input nodes of forecasting model are effectively reduced. Based on Bagging, the NNE constituted by five BP neural networks is employed to forecast GDP of Jiangmen, Guangdong with favorable results obtained, which shows that NNE is generally superior to simplex neural network, and valid and feasible for economic forecasting.
Supported by the National Natural Science Foundation of China under Grant 70471074 and Guangdong Provincial Department of Science and Technology under Grant 2004B36001051.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Xiao, J.H.: Intelligent Forecasting for the Regional Economic. Mathematics in Economics 22(1), 57–63 (2005)
Hansen, L.K., Salamon, P.: Neural Network Ensembles. IEEE Trans Pattern Analysis and Machine Intelligence 12(10), 993–1001 (1990)
Ding, L.Q., Yang, J.Q., Long, C.Y.: A Forecast Method of Generator’s Load Based on BP Artificial Neural Networks. In: Proceedings of 2005 International Conference on Management & Engineering (12th) Harbin, pp. 2319–2322 (2005)
Zhou, Z.H., Chen, S.F.: Neural Network Ensemble. Chinese J. Computers 25(1), 1–8 (2002)
Shen, X.H., Zhou, Z.H., Wu, J.X., Chen, Z.Q.: Survey of Boosting and Bagging. Computer Engineering and Application 12, 31–32 (2000)
Cui, Y.Q., Li, Z.B.: Application of an Improved Artificial Neural Network in Battlefield Ammunition Consumption Prediction. In: Proceedings of 2005 International Conference on Management & Engineering (12th) Harbin, pp. 182–186 (2005)
Cheng, Q.Y., Wang, Y.Y., Chen, W.G.: Modified Principal Component Analysis Based on Short-Term Load Forecasting. Power System Technology 29(3), 64–67 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lin, J., Zhu, B. (2006). Improved Principal Component Analysis and Neural Network Ensemble Based Economic Forecasting. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_14
Download citation
DOI: https://doi.org/10.1007/11816157_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
eBook Packages: Computer ScienceComputer Science (R0)