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Selection of the Appropriate Lag Structure of Foreign Exchange Rates Forecasting Based on Autocorrelation Coefficient

  • Wei Huang
  • Shouyang Wang
  • Hui Zhang
  • Renbin Xiao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

We propose a new criterion, called autocorrelation coefficient criterion (ACC) to select the appropriate lag structure of foreign exchange rates forecasting with neural networks, and design the corresponding algorithm. The criterion and algorithm are data-driven in that there is no prior assumption about the models for time series under study. We conduct the experiments to compare the prediction performance of the neural networks based on the different lag structures by using the different criterions. The experiment results show that ACC performs best in selecting the appropriate lag structure for foreign exchange rates forecasting with neural networks.

Keywords

Neural Network Normalize Mean Square Error Time Series Forecast Foreign Exchange Rate Schwarz Information Criterion 
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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wei Huang
    • 1
    • 2
  • Shouyang Wang
    • 2
  • Hui Zhang
    • 3
    • 4
  • Renbin Xiao
    • 1
  1. 1.School of ManagementHuazhong University of Science and TechnologyWuhanChina
  2. 2.Institute of Systems Science, Academy of Mathematics and Systems SciencesChinese Academy of SciencesBeijingChina
  3. 3.School of Knowledge ScienceJapan Advanced Institute of Science and TechnologyIshikawaJapan
  4. 4.School of Computer ScienceSouthwest University of Science and TechnologyMianyangChina

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