Work

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

Year: 2025

Type: article

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.

Source: Mathematics

DOI: 10.3390/math13091415

Authors: Zhiyuan Pei, Jianqi Yan, Jin Yan, Bailing Yang, Xin Liu

Institution: Macau University of Science and Technology

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: Fundo para o Desenvolvimento das Ciências e da Tecnologia, Wuyi University