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Comparisons of the Different Frequencies of Input Data for Neural Networks in Foreign Exchange Rates Forecasting

  • Wei Huang
  • Lean Yu
  • Shouyang Wang
  • Yukun Bao
  • Lin Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3994)

Abstract

We compare the predication performance of neural networks with the different frequencies of input data, namely daily data, weekly data, monthly data. In the 1 day and 1 week ahead prediction of foreign exchange rates forecasting, the neural networks with the weekly input data performs better than the random walk models. In the 1 month ahead prediction of foreign exchange rates forecasting, only the special neural networks with weekly input data perform better than the random walk models. Because the weekly data contain the appropriate fluctuation information of foreign exchange rates, it can balance the noise of daily data and losing information of monthly data.

Keywords

Neural Network Neural Network Model Prediction Performance Daily Data Monthly Data 
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
  • Lean Yu
    • 2
  • Shouyang Wang
    • 2
  • Yukun Bao
    • 1
  • Lin Wang
    • 1
  1. 1.School of ManagementHuazhong University of Science and TechnologyWuHanChina
  2. 2.Institute of Systems ScienceAcademy of Mathematics and Systems Sciences, Chinese Academy of SciencesBeijingChina

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