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Price Prediction of Bitcoin Based on Adaptive Feature Selection and Model Optimization

Author

Listed:
  • Yingjie Zhu

    (School of Science, Changchun University, Changchun 130022, China)

  • Jiageng Ma

    (School of Science, Changchun University, Changchun 130022, China)

  • Fangqing Gu

    (School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510520, China)

  • Jie Wang

    (School of Science, Changchun University, Changchun 130022, China)

  • Zhijuan Li

    (School of Science, Changchun University, Changchun 130022, China)

  • Youyao Zhang

    (School of Philosophy, Shaanxi Normal University, Xi’an 710119, China)

  • Jiani Xu

    (School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • Yifan Li

    (HSBC Business School, Peking University, Beijing 100871, China)

  • Yiwen Wang

    (School of Science, Changchun University, Changchun 130022, China)

  • Xiangqun Yang

    (School of Science, Changchun University, Changchun 130022, China)

Abstract

Bitcoin is one of the most successful cryptocurrencies, and research on price predictions is receiving more attention. To predict Bitcoin price fluctuations better and more effectively, it is necessary to establish a more abundant index system and prediction model with a better prediction effect. In this study, a combined prediction model with twin support vector regression was used as the main model. Twenty-seven factors related to Bitcoin prices were collected. Some of the factors that have the greatest impact on Bitcoin prices were selected by using the XGBoost algorithm and random forest algorithm. The combined prediction model with support vector regression (SVR), least-squares support vector regression (LSSVR), and twin support vector regression (TWSVR) was used to predict the Bitcoin price. Since the model’s hyperparameters have a great impact on prediction accuracy and algorithm performance, we used the whale optimization algorithm (WOA) and particle swarm optimization algorithm (PSO) to optimize the hyperparameters of the model. The experimental results show that the combined model, XGBoost-WOA-TWSVR, has the best prediction effect, and the EVS score of this model is significantly better than that of the traditional statistical model. In addition, our study verifies that twin support vector regression has advantages in both prediction effect and computation speed.

Suggested Citation

  • Yingjie Zhu & Jiageng Ma & Fangqing Gu & Jie Wang & Zhijuan Li & Youyao Zhang & Jiani Xu & Yifan Li & Yiwen Wang & Xiangqun Yang, 2023. "Price Prediction of Bitcoin Based on Adaptive Feature Selection and Model Optimization," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1335-:d:1092703
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    References listed on IDEAS

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    Cited by:

    1. Oluwadamilare Omole & David Enke, 2024. "Deep learning for Bitcoin price direction prediction: models and trading strategies empirically compared," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-26, December.

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