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Dimensionality Reduction by Turning Points for Stream Time Series Prediction

  • Van VoEmail author
  • Luo Jiawei
  • Bay Vo
Conference paper
  • 1.1k Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 457)

Abstract

In recent years, there has been an explosion of problems concerned with mining time series databases and dimensionality reduction is one of the important tasks in time series data mining analysis. These approaches are very useful to pre-process the large dataset and then use it to analyze and mine. In this paper, we propose a method based on turning points to reduce the dimensions of stream time series data, this task helps the prediction process faster. The turning points which are extracted from the maximum or minimum points of the time series stream are proved more efficient and effective in preprocessing data for stream time series prediction. To implement the proposed framework, we use stock time series obtained from Yahoo Finance, the prediction approach based on Sequential Minimal Optimization and the experimental results validate the effectiveness of our approach.

Keywords

Stream Mining Time Series Dimensionality Reduction Turning Points Stream Time Series Time Series Prediction 

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References

  1. 1.
    Fu, T.: A review on time series data mining. Engineering Applications of Artificial Intelligence 24(1), 164–181 (2011)CrossRefGoogle Scholar
  2. 2.
    Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems 3(3), 263–286 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Lendasse, A., Lee, J., Bodt, E.D., Wertz, V., Verleysen, M.: Dimension reduction of technical indicators for the prediction of financial time series - Application to the BEL20 Market Index. European Journal of Economic and Social Systems 15(2), 31–48 (2001)zbMATHCrossRefGoogle Scholar
  4. 4.
    Wang, Q., Megalooikonomou, V.: A dimensionality reduction technique for efficient time series similarity analysis. Information Systems 33(1), 115–132 (2008)CrossRefGoogle Scholar
  5. 5.
    Zhang, Z., Li, J., Wang, H., Wang, S.: Study of principal component analysis on multi-dimension stock data. Chinese Journal of Scientific Instrument 26(8), 2489–2491 (2005)Google Scholar
  6. 6.
    Fu, T.-C., Chung, F.-L., Luk, R., Ng, C.-M.: Representing financial time series based on data point importance. Engineering Applications of Artificial Intelligence 21(2), 277–300 (2008)CrossRefGoogle Scholar
  7. 7.
    Bao, D.: A generalized model for financial time series representation and prediction. Applied Intelligence 29(1), 1–11 (2008)CrossRefGoogle Scholar
  8. 8.
    Bao, D., Yang, Z.: Intelligent stock trading system by turning point confirming and probabilistic reasoning. Expert Systems with Applications 34(1), 620–627 (2008)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Yang, J.F., Zhai, Y.J., Xu, D.P., Han, P.: SMO algorithm applied in time series model building and forecast. In: Proc. of the 6th International Conference on Machine Learning and Cybernetics, Hong Kong, vol. 4, pp. 2395–2400 (2007)Google Scholar
  10. 10.
    Osuna, E., Freund, R.: An improved training algorithm for support vector machines. In: Proceedings of the 1997 IEEE Neural Networks for Signal Processing VII, New York, pp. 276–285. IEEE (1997)Google Scholar
  11. 11.
    Platt, J.C.: Sequential Minimal Optimization: A fast algorithm for training support vector machines. Advances in kernel methods, pp. 185–208. MIT Press, Cambridge (1999)Google Scholar
  12. 12.
    Chen, Z., Yang, Y.: Assessing Forecast Accuracy Measures (2004), http://www.stat.iastate.edu/preprint/articles/2004-10.pdf
  13. 13.
    Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. In: Proceedings of ACM SIGMOD Conference on Management of Data, pp. 151–162 (2001)Google Scholar
  14. 14.
    Nguyen, N.T.: Inconsistency of Knowledge and Collective Intelligence. Cybernetics and Systems 39(6), 542–562 (2008)zbMATHCrossRefGoogle Scholar
  15. 15.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Ian, H.: The WEKA data mining software: an update. SIGKDD Explorations 11(1), 10–18 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Information Science and EngineeringHunan UniversityChangshaChina
  2. 2.Faculty of Information TechnologyHo Chi Minh University of IndustryHo Chi MinhVietnam
  3. 3.Information Technology CollegeHo Chi MinhVietnam

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