Time-Varying Sequence Model

Sneha Jadhav, Jianxiang Zhao, Yepeng Fan, Jingjing Li, Hao Lin, Chenggang Yan () and Minghan Chen ()
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Sneha Jadhav: Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, NC 27109, USA
Jianxiang Zhao: Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou 310018, China
Yepeng Fan: Department of Mathematics and Statistics, 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

Mathematics, 2023, vol. 11, issue 2, 1-15

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.

Keywords: sequence model; basis expansion; dynamic weight update; neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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