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Financial sequence prediction based on swarm intelligence algorithms and internet of things

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Abstract

This paper aims to predict the financial time series. Swarm intelligence algorithms usually use metadata to ensure objectivity without the statistical assumptions of the data. This paper proposed a prediction algorithm integrating multiple support vector regression (SVR) models. The algorithm selects different datasets to train these SVR models. This algorithm also adopts reasonable weights to combine the forecasting results of multiple models to reduce the overall prediction error. The weight of each model is dynamically adjusted according to its recent prediction accuracy. Therefore, this algorithm is adaptive and can deal with nonstationary problems. Five international authoritative stock indexes are used to compare the hybrid SVR model with a single SVR model for performance validation from the perspectives of normalized mean squared error, weighted directional symmetry, and root mean squared error. The results demonstrate that the hybrid SVR model has significantly improved the prediction accuracy and generalization ability of the prediction algorithm compared with a single SVR model. It reveals that selecting the appropriate input vector can achieve an excellent prediction effect.

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The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

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Acknowledgements

The authors acknowledge the help from the university colleagues.

Funding

This work was supported by IPIS2012, the authors would like to thank the National Social Science Foundation of China (20CZZ017).

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Authors and Affiliations

  1. School of Economics and Management, Shandong Agricultural University, Taian, 271000, China

    Zheng Gao

  2. Department of Industrial and Enterprise Systems Engineering, University of Illinois Urbana-Champaign, Champaign, IL, 61820, USA

    Chenxiang Zhang

  3. Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Zhengyin Li

Authors
  1. Zheng Gao
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  2. Chenxiang Zhang
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  3. Zhengyin Li
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Correspondence to Zhengyin Li.

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Gao, Z., Zhang, C. & Li, Z. Financial sequence prediction based on swarm intelligence algorithms and internet of things. J Supercomput 78, 17470–17490 (2022). https://doi.org/10.1007/s11227-022-04572-7

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