首页|RVFL-LSTM: A lightweight model with long-short term memory for time series

RVFL-LSTM: A lightweight model with long-short term memory for time series

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  • NETL
  • NSTL
  • Elsevier
© 2024 Elsevier B.V.Neural networks have been widely used for time series prediction due to their excellent ability to capture the sequential relationship between the past and the future in time series data. However, the existing neural networks, such as long-short term memory(LSTM) and recurrent ones, are often criticized for their complex structure and strict training mode, but some lightweight network models often cannot achieve satisfying prediction performance. In order to tackle this challenge, this paper proposes a lightweight model with long-short term memory for time series, named RVFL-LSTM, which is trained for moving auto-regression task. To highlight the long and short-term patterns in time series data, RVFL-LSTM adjusts the input weights to learn the short-term patterns and updates the output weights gradually to capture the long-term patterns. Experimental results show that the proposed method can capture the long-term and short-term patterns efficiently, and it is very competitive in time series prediction with respect to prediction accuracy and computational complexity.

Auto-regressionLong short-term memory (LSTM)Online sequential extreme learning machine (OS-ELM)Random Vector Functional Link (RVFL)Time series prediction

Liu Q.、Wang Q.、Wang X.

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Big Data Institute College of Computer Science and Software Engineering Shenzhen University

Big Data Institute College of Computer Science and Software Engineering Shenzhen UniversityBig Data Institute College of Computer Science and Software Engineering Shenzhen University||The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University||

2025

10.1016/j.knosys.2024.112896

Knowledge-based systems

Knowledge-based systems

SCI
ISSN：0950-7051
年,卷(期)：2025.309(Jan.30)
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