An Intelligent Weighted Fuzzy Time Series Model Based on a Sine-Cosine Adaptive Human Learning Optimization Algorithm and Its Application to Financial Markets Forecasting | SpringerLink
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An Intelligent Weighted Fuzzy Time Series Model Based on a Sine-Cosine Adaptive Human Learning Optimization Algorithm and Its Application to Financial Markets Forecasting

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Advanced Data Mining and Applications (ADMA 2017)

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Abstract

Financial forecasting is an extremely challenging task given the complex, nonlinear nature of financial market systems. To overcome this challenge, we present an intelligent weighted fuzzy time series model for financial forecasting, which uses a sine-cosine adaptive human learning optimization (SCHLO) algorithm to search for the optimal parameters for forecasting. New weighted operators that consider frequency based chronological order and stock volume are analyzed, and SCHLO is integrated to determine the effective intervals and weighting factors. Furthermore, a novel short-term trend repair operation is developed to complement the final forecasting process. Finally, the proposed model is applied to four world major trading markets: the Dow Jones Index (DJI), the German Stock Index (DAX), the Japanese Stock Index (NIKKEI), and Taiwan Stock Index (TAIEX). Experimental results show that our model is consistently more accurate than the state-of-the-art baseline methods. The easy implementation and effective forecasting performance suggest our proposed model could be a favorable market application prospect.

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Acknowledgements

This material is based upon work supported in whole or in part with funding from the Laboratory for Analytic Sciences (LAS). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the LAS and/or any agency or entity of the United States Government.

Author information

Authors and Affiliations

  1. North Carolina State University, Raleigh, NC, USA

    Ruixin Yang, Mingyang Xu, Stephen Ranshous & Nagiza F. Samatova

  2. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Nagiza F. Samatova

  3. Shanghai University, Shanghai, China

    Junyi He

Authors
  1. Ruixin Yang
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  2. Mingyang Xu
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  3. Junyi He
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  4. Stephen Ranshous
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  5. Nagiza F. Samatova
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Corresponding author

Correspondence to Nagiza F. Samatova .

Editor information

Editors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Gao Cong

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

  3. Macquarie University, Sydney, New South Wales, Australia

    Wei Emma Zhang

  4. Wuhan University, Wuhan, China

    Chengliang Li

  5. Nanyang Technological University, Singapore, Singapore

    Aixin Sun

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Yang, R., Xu, M., He, J., Ranshous, S., Samatova, N.F. (2017). An Intelligent Weighted Fuzzy Time Series Model Based on a Sine-Cosine Adaptive Human Learning Optimization Algorithm and Its Application to Financial Markets Forecasting. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_42

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  • DOI: https://doi.org/10.1007/978-3-319-69179-4_42

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-69179-4

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