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Stock Index Prediction Based on Adaptive Training and Pruning Algorithm

  • Jinyuan Shen
  • Huaiyu Fan
  • Shengjiang Chang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4492)

Abstract

A tapped delay neural network (TDNN) with an adaptive learning and pruning algorithm is proposed to predict the nonlinear time serial stock indexes. The TDNN is trained by the recursive least square (RLS) in which the learning-rate parameter can be chosen automatically. This results in the network converging fast. Subsequently the architecture of the trained neural network is optimized by utilizing pruning algorithm to reduce the computational complexity and enhance the network’s generalization. And then the optimized network is retrained so that it has optimum parameters. At last the test samples are predicted by the ultimate network. The simulation and comparison show that this optimized neuron network model can not only reduce the calculating complexity greatly, but also improve the prediction precision. In our simulation, the computational complexity is reduced to 0.0556 and mean square error of test samples reaches 8.7961×10− 5.

Keywords

Hide Layer Input Layer Stock Index Recursive Less Square Kernel Principal Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Jinyuan Shen
    • 1
  • Huaiyu Fan
    • 1
  • Shengjiang Chang
    • 2
  1. 1.School of Information Engineering, Zhengzhou University, ZhengzhouChina
  2. 2.Institute of Modern Optics, Nankai University, TianjinChina

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