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Time Series Forecasting

GluTANN: Transformer-Based Continuous Glucose Monitoring Model with ANN Attention

In this work, we propose an innovative model based on Transformer architecture, GluTANN, with specially designed ANNs acting as self-attentions and paired correlations preserved by the encoder-decoder structure. Extensive experiments across five recognized datasets demonstrate that GluTANN has great competitiveness in reducing uncertainty while preserving satisfying accuracy, providing a feasible approach to effective glucose management and diabetes medical decisions
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International Conferences (Peer-reviewed)

  1. Sijie Xiong, Youhao Xu, Cheng Tang, Jianing Wang, Shuqing Liu, Atsushi Shimada
    GluTANN: Transformer-Based Continuous Glucose Monitoring Model with ANN Attention
    2025 IEEE Conference on Artificial Intelligence (CAI), pp.543-548, 2025.05
    BibTeX

Enhancing nonlinear dependencies of Mamba via negative feedback for time series forecasting

In this work, we are inspired by the curvature from financial domains and control systems, proposing CME-Mamba. The effectiveness, stability, robustness, etc., are discussed. Extensive experiments demonstrate that CME-Mamba grows excellent to uncover complex paradigms and predict future states in various domains, especially improving the performance for periodic and high-variate situations.
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Journals (Peer-reviewed)

  1. Sijie Xiong, Cheng Tang, Yuanyuan Zhang, Haoling Xiong, Youhao Xu, Atsushi Shimada
    Enhancing Nonlinear Dependencies of Mamba via Negative Feedback for Time Series Forecasting
    Applied Soft Computing, Vol.184, p.113758, 2025.08
    BibTeX