A Review of Research on Time Series Classification Based on Deep Learning

REN Liqiang; JIA Shuyi; WANG Haipeng; WANG Ziling

doi:10.11999/JEIT231222

Volume 46 Issue 8

Aug. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(8): 3094-3116

REN Liqiang, JIA Shuyi, WANG Haipeng, WANG Ziling. A Review of Research on Time Series Classification Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3094-3116. doi: 10.11999/JEIT231222

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REN Liqiang, JIA Shuyi, WANG Haipeng, WANG Ziling. A Review of Research on Time Series Classification Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3094-3116. doi: 10.11999/JEIT231222

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A Review of Research on Time Series Classification Based on Deep Learning

doi: 10.11999/JEIT231222 cstr: 32379.14.JEIT231222

Naval Aviation University, Yantai 264001, China

Funds: The National natural Science Foundation of China (62076249), The natural Science Foundation of Shandong Province (ZR2020MF154)

Received Date: 2023-11-03
Rev Recd Date: 2023-12-21

Available Online: 2023-12-27

Publish Date: 2024-08-30

Abstract

Abstract

Time Series Classification(TSC) is one of the most important and challenging tasks in the field of data mining. Deep learning techniques have achieved revolutionary progress in natural language processing and computer vision, and have also demonstrated great potential in areas such as time series analysis. A detailed review of the latest research advances in deep learning-based TSC is provided in this paper. Firstly, key terms and related concepts are defined. Secondly, the latest time series classification models are classified from four perspectives of network architectures: multilayer perceptron, convolutional neural networks, recurrent neural networks, and attention mechanisms, along with their respective advantages and limitations. Additionally, the latest developments and challenges in time series classification in the fields of human activity recognition and electroencephalogram-based emotion recognition are outlined. Finally, the unresolved issues and future research directions when applying deep learning to time series data are discussed. This paper provides researchers with a reference for understanding the latest research dynamics, new technologies, and development trends in the deep learning-based time series classification field.
- Deep learning,
- Time series,
- Neural networks,
- Classification,
- Review

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References(142)

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