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Time-Varying Sequence Model

Author

Listed:
  • Sneha Jadhav

    (Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, NC 27109, USA
    These authors contributed equally to this work.)

  • Jianxiang Zhao

    (Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou 310018, China
    These authors contributed equally to this work.)

  • Yepeng Fan

    (Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, NC 27109, USA
    Department of Computer Science, Wake Forest University, Winston-Salem, NC 27109, USA)

  • Jingjing Li

    (Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, NC 27109, USA)

  • Hao Lin

    (Department of Electrical and Computer Engineering, Duke University, Durham, NC 27705, USA)

  • Chenggang Yan

    (Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Minghan Chen

    (Department of Computer Science, Wake Forest University, Winston-Salem, NC 27109, USA)

Abstract

Traditional machine learning sequence models, such as RNN and LSTM, can solve sequential data problems with the use of internal memory states. However, the neuron units and weights are shared at each time step to reduce computational costs, limiting their ability to learn time-varying relationships between model inputs and outputs. In this context, this paper proposes two methods to characterize the dynamic relationships in real-world sequential data, namely, the internal time-varying sequence model (ITV model) and the external time-varying sequence model (ETV model). Our methods were designed with an automated basis expansion module to adapt internal or external parameters at each time step without requiring high computational complexity. Extensive experiments performed on synthetic and real-world data demonstrated superior prediction and classification results to conventional sequence models. Our proposed ETV model is particularly effective at handling long sequence data.

Suggested Citation

  • Sneha Jadhav & Jianxiang Zhao & Yepeng Fan & Jingjing Li & Hao Lin & Chenggang Yan & Minghan Chen, 2023. "Time-Varying Sequence Model," Mathematics, MDPI, vol. 11(2), pages 1-15, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:336-:d:1029342
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    References listed on IDEAS

    as
    1. John A. Rice & Colin O. Wu, 2001. "Nonparametric Mixed Effects Models for Unequally Sampled Noisy Curves," Biometrics, The International Biometric Society, vol. 57(1), pages 253-259, March.
    2. J. Ramsay, 1982. "When the data are functions," Psychometrika, Springer;The Psychometric Society, vol. 47(4), pages 379-396, December.
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    Cited by:

    1. Guoqiang Sun & Xiaoyan Qi & Qiang Zhao & Wei Wang & Yujun Li, 2024. "SVSeq2Seq: An Efficient Computational Method for State Vectors in Sequence-to-Sequence Architecture Forecasting," Mathematics, MDPI, vol. 12(2), pages 1-17, January.
    2. Jianrong Chen & Xiangui Kang & Yunong Zhang, 2023. "Continuous and Discrete ZND Models with Aid of Eleven Instants for Complex QR Decomposition of Time-Varying Matrices," Mathematics, MDPI, vol. 11(15), pages 1-18, July.

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