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Financial Sequence Prediction Based on Swarm Intelligence Algorithms of Internet of Things

Author

Listed:
  • Jinquan Liu

    (Jilin University)

  • Yupin Wei

    (Jilin University)

  • Hongzhen Xu

    (Northeast Normal University)

Abstract

In order to accurately predict the financial time series and stock price fluctuation, in this study, metadata was used to ensure the objectivity of data for most of the swarm intelligence algorithms of the Internet of Things. The results show that the combination of theoretical methods and financial time series forecasting model can significantly improve the forecasting effect. At the same time, the proposed fuzzy theory also gives a totally different perspective to the modeling of financial time series which has stochastic and uncertain characteristics. Therefore, swarm intelligence algorithm and fuzzy theory are effectively combined, and their combination is very useful for the improvement of two financial fuzzy time series models. Because of their combination, the problem of stock price forecasting with different orders and different factors is well solved. Besides, it also provides two new solutions for such problems respectively as well as additional model choices for investors to avoid risks and improve returns.

Suggested Citation

  • Jinquan Liu & Yupin Wei & Hongzhen Xu, 2022. "Financial Sequence Prediction Based on Swarm Intelligence Algorithms of Internet of Things," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1465-1480, April.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:4:d:10.1007_s10614-020-10079-1
    DOI: 10.1007/s10614-020-10079-1
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    References listed on IDEAS

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    1. Yan Ye & Jingfeng Li & Kaibin Li & Hui Fu, 2018. "Cross-docking truck scheduling with product unloading/loading constraints based on an improved particle swarm optimisation algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 56(16), pages 5365-5385, August.
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