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)


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.


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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  1. 1.
    Lai, K.K., Yu, L.A., Wang, S.Y.: A Neural Network and Web-Based Decision Support System for Forex Forecasting and Trading. In: Shi, Y., Xu, W., Chen, Z. (eds.) CASDMKM 2004. LNCS (LNAI), vol. 3327, pp. 243–253. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Huang, W., Lai, K.K., Nakamori, Y., Wang, S.Y.: Forecasting Foreign Exchange Rates with Artificial Neural Networks: a Review. International Journal of Information Technology & Decision Making 3(1), 145–165 (2004)CrossRefGoogle Scholar
  3. 3.
    Zhang, H., Ho, T.B., Huang, W.: Blind Feature Extraction for Time-Series Classification Using Haar Wavelet Transform. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 605–610. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Yu, L.A., Wang, S.Y., Lai, K.K.: Adaptive Smoothing Neural Networks in Foreign Ex-change Rate Forecasting. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3516, pp. 523–530. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Yu, L.A., Wang, S.Y., Lai, K.K.: A Novel Nonlinear Ensemble Forecasting Model Incor-porating GLAR and ANN for Foreign Exchange Rates. Computers & Operations Research 32, 2523–2541 (2005)MATHCrossRefGoogle Scholar
  6. 6.
    Yu, L.A., Lai, K.K., Wang, S.Y.: Double Robustness Analysis for Determining Optimal Feedforward Neural Network Architecture. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 382–385. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Huang, W., Nakamori, Y., Wang, S.Y.: Forecasting Stock Market Movement Direction with Support Vector Machine. Computers & Operations Research 32, 2513–2522 (2005)MATHCrossRefGoogle Scholar
  8. 8.
    Ivanov, V., Kilian, L.: A Practitioner’s Guide to Lag-Order Selection for Vector Autore-gressions. Working paper, Centre for Economic Policy Research, 90-98 (2000)Google Scholar
  9. 9.
    Kapetanios, G.: Model Selection in Threshold Models. Journal of Time Series Analysis 22, 733–754 (2001)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Ng, S., Perron, P.: Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power. Boston College Working Papers in Economics 369, Boston College Department of Economics (2000)Google Scholar
  11. 11.
    Ozcicek, O., Mcmillian, W.D.: Lag Length Selection in Vector Autoregressive Models: Symmetric and Asymmetric Lags. Applied Economics 31, 517–524 (1999)CrossRefGoogle Scholar
  12. 12.
    Qi, M., Zhang, G.P.: An Investigation of Model Selection Criteria for Neural Network Time Series Forecasting. European Journal of Operational Research 132, 666–680 (2001)MATHCrossRefGoogle Scholar
  13. 13.
    Tschernig, R., Yang, L.: Nonparametric Lag Selection for Time Series. Journal of Time Series Analysism 21, 457–487 (2000)MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Zhang, G.P.: Neural Networks in Business Forecasting. Idea Group Inc. (2003)Google Scholar
  15. 15.
    Huang, W., Nakamori, Y., Wang, S.Y., Zhang, H.: Select the Size of Training Set for Fi-nancial Forecasting with Neural Networks. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 879–884. Springer, Heidelberg (2005)CrossRefGoogle Scholar

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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