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Multi-Scale TsMixer: A Novel Time-Series Architecture for Predicting A-Share Stock Index Futures

Author

Listed:
  • Zhiyuan Pei

    (School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, China)

  • Jianqi Yan

    (School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, China)

  • Jin Yan

    (School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, China)

  • Bailing Yang

    (School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, China)

  • Xin Liu

    (Macau Institute of Systems Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, China)

Abstract

With the advancement of deep learning, its application in financial market forecasting has become a research hotspot. This paper proposes an innovative Multi-Scale TsMixer model for predicting stock index futures in the A-share market, covering SSE50, CSI300, and CSI500. By integrating Multi-Scale time-series features across the short, medium, and long term, the model effectively captures market fluctuations and trends. Moreover, since stock index futures reflect the collective movement of their constituent stocks, we introduce a novel approach: predicting individual constituent stocks and merging their forecasts using three fusion strategies (average fusion, weighted fusion, and weighted decay fusion). Experimental results demonstrate that the weighted decay fusion method significantly improves the prediction accuracy and stability, validating the effectiveness of Multi-Scale TsMixer.

Suggested Citation

  • Zhiyuan Pei & Jianqi Yan & Jin Yan & Bailing Yang & Xin Liu, 2025. "Multi-Scale TsMixer: A Novel Time-Series Architecture for Predicting A-Share Stock Index Futures," Mathematics, MDPI, vol. 13(9), pages 1-19, April.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:9:p:1415-:d:1642581
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    References listed on IDEAS

    as
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