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Forecasting the TAIEX Based on Fuzzy Time Series, PSO Techniques and Support Vector Machines

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Intelligent Information and Database Systems (ACIIDS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7802))

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Abstract

This paper presents a new method for forecasting the TAIEX based on fuzzy time series, particle swarm optimization techniques and support vector machines. The proposed method to forecast the TAIEX is based on slope of one-day variations of the TAIEX and the slope of two-days average variations of the TAIEX. The particle swarm optimization techniques are used to get optimal intervals in the universe of discourse. The support vector machine is used to classify the training data set. The experimental results show that the proposed method outperforms the existing methods for forecasting the TAIEX.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C.

    Shyi-Ming Chen & Pei-Yuan Kao

Authors
  1. Shyi-Ming Chen
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  2. Pei-Yuan Kao
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Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Systems, Department of Software Engineering, Universiti Teknologi Malaysia, 81310, Johar Baharu, Johor, Malaysia

    Ali Selamat  & Habibollah Haron  & 

  2. Institute of Informatics, Division of Knowledge Managements Systems, Wrocław University of Technology, Str. Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Ngoc Thanh Nguyen

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Chen, SM., Kao, PY. (2013). Forecasting the TAIEX Based on Fuzzy Time Series, PSO Techniques and Support Vector Machines. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36546-1_10

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  • DOI: https://doi.org/10.1007/978-3-642-36546-1_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36545-4

  • Online ISBN: 978-3-642-36546-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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