Version 1
: Received: 4 April 2024 / Approved: 4 April 2024 / Online: 4 April 2024 (12:13:54 CEST)
How to cite:
Cai, W.; Ji, Z.; Feng, Q. A Temporal Difference and Cross-variate Fusion Network for Multivariate Time Series Classification. Preprints2024, 2024040375. https://doi.org/10.20944/preprints202404.0375.v1
Cai, W.; Ji, Z.; Feng, Q. A Temporal Difference and Cross-variate Fusion Network for Multivariate Time Series Classification. Preprints 2024, 2024040375. https://doi.org/10.20944/preprints202404.0375.v1
Cai, W.; Ji, Z.; Feng, Q. A Temporal Difference and Cross-variate Fusion Network for Multivariate Time Series Classification. Preprints2024, 2024040375. https://doi.org/10.20944/preprints202404.0375.v1
APA Style
Cai, W., Ji, Z., & Feng, Q. (2024). A Temporal Difference and Cross-variate Fusion Network for Multivariate Time Series Classification. Preprints. https://doi.org/10.20944/preprints202404.0375.v1
Chicago/Turabian Style
Cai, W., Zinan Ji and Qihong Feng. 2024 "A Temporal Difference and Cross-variate Fusion Network for Multivariate Time Series Classification" Preprints. https://doi.org/10.20944/preprints202404.0375.v1
Abstract
Multivariate Time Series Classification (MTSC) is one of most important tasks in time series analysis, aiding in activities such as human motion recognition and medical diagnostics. Existing methods for MTSC do not explicitly model temporal differences and generalize the idea of temporal difference into a efficient temporal module. Additionally, existing methods are not yet able to capture cross-variable relationships well during network training. As a result, they are unable to achieve convincing feature representation, leading to suboptimal classification accuracy. In this paper, we propose a novel MTSC model called Temporal Difference and Cross-variate Fusion Network (TDCFN), which integrates a two-stream differential LSTM network and a cross-variate feature extraction network to enhance feature representation. TDCFN achieves superior classification accuracy by capturing dynamic temporal evolution and inter-variable relationships. The experimental results show that TDCFN can achieve competitive performance with state-of-the-art multivariate time series classification approaches.
Keywords
Multivariate Time Series Classification; Human Motion Recognition; Human Activity Recognition; Medical Signal Classification; Two-stream Model; Depthwise Separable Convolution; Differential LSTM network
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
