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.
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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
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DOI: https://doi.org/10.1007/11816157_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
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