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Stock Price Forecasting with Empirical Mode Decomposition Based Ensemble \(\nu \)-Support Vector Regression Model

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Computational Intelligence, Communications, and Business Analytics (CICBA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 775))

Abstract

Stock price forecasting is one of the most challenging tasks of time series forecasting due to the inherent non-linearity and non-stationary characteristics of the stock market and financial time series. In this paper, an ensemble method composed of Empirical Mode Decomposition (EMD) algorithm and \(\nu \)-Support Vector Regression (\(\nu \)-SVR) is presented for short-term stock price forecasting. First of all, the historical stock price time series were decomposed into several intrinsic mode functions (IMFs). Then each IMF was modeled by a \(\nu \)-SVR model to generate the corresponding forecasting IMF value. Finally, the prediction results of all IMFs were combined to formulate an aggregated output for stock price. The stock market price datasets of three power related companies are used to test the effectiveness of the proposed EMD-\(\nu \)-SVR method. Simulation results demonstrated attractiveness of the proposed method compared with six forecasting methods.

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Acknowledgment

This project is funded by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.

Author information

Authors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Xueheng Qiu, Huilin Zhu & P. N. Suganthan

  2. University of Cambridge, Cambridge, UK

    Gehan  A. J.  Amaratunga

Authors
  1. Xueheng Qiu
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  2. Huilin Zhu
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  3. P. N. Suganthan
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  4. Gehan  A. J.  Amaratunga
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Corresponding author

Correspondence to P. N. Suganthan .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Computer and System Sciences, Visva Bharati University, Bolpur Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Information Technology, Calcutta Business School, Kolkata, India

    Somnath Mukhopadhyay

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Qiu, X., Zhu, H., Suganthan, P.N., Amaratunga, G.A.J. (2017). Stock Price Forecasting with Empirical Mode Decomposition Based Ensemble \(\nu \)-Support Vector Regression Model. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 775. Springer, Singapore. https://doi.org/10.1007/978-981-10-6427-2_2

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  • DOI: https://doi.org/10.1007/978-981-10-6427-2_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6426-5

  • Online ISBN: 978-981-10-6427-2

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