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Fuzzy Support Vector Machines Regression for Business Forecasting: An Application

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Fuzzy Systems and Knowledge Discovery (FSKD 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4223))

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Abstract

This study proposes a novel method for business forecasting based on fuzzy support vector machines regression (FSVMR). By an application on sales forecasting, details of proposed method are presented including data preprocessing, kernel selection, parameters tuning and so on. The experimental result shows the method’s validity.

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Author information

Authors and Affiliations

  1. Department of Management Science & Information System, School of Management, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yukun Bao & Rui Zhang

  2. Department of Management Science, Management School, Lancaster University, Lancaster, LA1 4YX, United Kingdom

    Yukun Bao & Sven F. Crone

Authors
  1. Yukun Bao
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  2. Rui Zhang
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  3. Sven F. Crone
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University,, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. Life Science Research Center, School of Electronic Engineering, Xidian University,, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  3. School of Electrical and Electronic Engineering, Xidian University, 710071, Xi’an, China

    Guanming Shi

  4. School of Information Technology and Electrical Engineering, The University of Queensland, 4072, Brisbane, Queensland, Australia

    Xue Li

  5. College of Mathematics and Information Science, Hebei Normal University, 050016, Shijiazhuang, Hebei, P.R. China

    Jing Liu

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© 2006 Springer-Verlag Berlin Heidelberg

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Bao, Y., Zhang, R., Crone, S.F. (2006). Fuzzy Support Vector Machines Regression for Business Forecasting: An Application. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_163

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  • DOI: https://doi.org/10.1007/11881599_163

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45916-3

  • Online ISBN: 978-3-540-45917-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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