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Support Vector Regression Parameters Optimization using Golden Sine Algorithm and its application in stock market

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  • Mohammadreza Ghanbari
  • Mahdi Goldani

Abstract

Support vector machine modeling is a new approach in machine learning for classification showing good performance on forecasting problems of small samples and high dimensions. Later, it promoted to Support Vector Regression (SVR) for regression problems. A big challenge for achieving reliable is the choice of appropriate parameters. Here, a novel Golden sine algorithm (GSA) based SVR is proposed for proper selection of the parameters. For comparison, the performance of the proposed algorithm is compared with eleven other meta-heuristic algorithms on some historical stock prices of technological companies from Yahoo Finance website based on Mean Squared Error and Mean Absolute Percent Error. The results demonstrate that the given algorithm is efficient for tuning the parameters and is indeed competitive in terms of accuracy and computing time.

Suggested Citation

  • Mohammadreza Ghanbari & Mahdi Goldani, 2021. "Support Vector Regression Parameters Optimization using Golden Sine Algorithm and its application in stock market," Papers 2103.11459, arXiv.org.
    • Handle: RePEc:arx:papers:2103.11459
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    File URL: http://arxiv.org/pdf/2103.11459
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    References listed on IDEAS

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    1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    2. Amirmohammad Tavakkoli & Jalal Rezaeenour & Esmaeil Hadavandi, 2015. "A Novel Forecasting Model Based on Support Vector Regression and Bat Meta-Heuristic (Bat–SVR): Case Study in Printed Circuit Board Industry," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(01), pages 195-215.
    3. Chen, Yun & Yang, Hui, 2012. "Multiscale recurrence analysis of long-term nonlinear and nonstationary time series," Chaos, Solitons & Fractals, Elsevier, vol. 45(7), pages 978-987.
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    Cited by:

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