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Improved Principal Component Analysis and Neural Network Ensemble Based Economic Forecasting

  • Jian Lin
  • Bangzhu Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jian Lin
    • 1
  • Bangzhu Zhu
    • 1
    • 2
  1. 1.Institute of System Science and TechnologyWuyi UniversityJiangmenChina
  2. 2.School of Economics and ManagementBeijing University of Aeronautics and AstronauticsBeijingChina

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