A Review of Research on Time Series Classification Based on Deep Learning
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摘要: 时间序列分类(TSC)是数据挖掘领域中最重要且最具有挑战性的任务之一。深度学习技术在自然语言处理和计算机视觉领域已取得革命性进展，同时在时间序列分析等其他领域也显示出巨大的潜力。该文对基于深度学习的时间序列分类的最新研究成果进行了详细综述。首先，定义了关键术语和相关概念。其次，从多层感知机、卷积神经网络、循环神经网络和注意力机制四个网络架构角度分类总结了当前最新的时间序列分类模型，及各自优点和局限性。然后，概述了时间序列分类在人体活动识别和脑电图情绪识别两个关键领域的最新进展和挑战。最后，讨论了将深度学习应用于时间序列数据时未解决的问题和未来研究方向。该文为研究者了解最新基于深度学习的时间序列分类研究动态、新技术和发展趋势提供了参考。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.
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Key words:
- Deep learning /
- Time series /
- Neural networks /
- Classification /
- Review
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表 1 UCR和UEA时间序列数据集详细信息
数据集 维度 数量 类别数量 训练集大小 序列长度 类型 UCR 1 128 2～60 16～8926 24～2 709 图像轮廓、传感器读数、动作分类、心电图、电子设备和模拟数据等 UEA 2-1345 30 2～39 12～30 000 8～17 984 心电图、运动分类、光谱分类等 
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表 2 基于CNN的时间序列分类模型总结
模型 提出年份 基准架构 模型特点 自适应模型 MC-DCNN[26] 2014 2-Stage Conv 每个通道独立卷积 MC-CNN[27] 2015 3-Stage Conv 所有通道1D卷积 Zhao et al.[28] 2015 2-Stage Conv 所有通道1D卷积 FCN[11] 2017 FCN 使用GAP替代FC层 ResNet[11] 2017 ResNet 9 使用3个残差块 Res-CNN[32] 2019 ResNet+FCN 使用1个残差块+FCN DCNNs[34] 2019 4-Stage Conv 使用扩张卷积 Disjoint-CNN[35] 2021 4-Stage Conv 分离型时空卷积 时间序列转换为图像 Wang&Oates[36] 2015 Tiled CNN 格拉姆角场和马尔可夫转移场图像编码 Hatami et al.[37] 2017 2-Stage Conv 递归图图像编码 Karimi et al.[38] 2018 Inception V3 格拉姆差角场图像编码 RPMCNN[41] 2019 VGGNet, 2-Stage Conv 相对位置矩阵图像编码 Yang et al.[39] 2019 VGGNet 格拉姆差角场、格拉姆加和场和马尔可夫转移场图像编码 多尺度卷积操作 MCNN[43] 2016 2-Stage Conv 恒等映射、下采样和平滑预处理 t-LeNet[24] 2016 2-Stage Conv 挤压和扩展预处理 MVCNN[46] 2019 4-stage Conv 基于Inception V1卷积 Inception-ResNet[47] 2021 ResNet 基于Inception V1卷积 InceptionTime[9] 2019 Inception V4 多分类器集成模型 EEG-inception[48] 2021 InceptionTime Inception-FCN[49] 2021 InceptionTime + FCN MRes-FCN[50] 2022 FCN + ResNet 使用多个串行多尺度卷积核
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表 3 基于注意力的时间序列分类模型总结
模型 提出年份 Embedding 注意力 自适应模型 MuVAN[78] 2018 Bi-GRU 注意力 ChannelAtt[81] 2018 RNN 注意力 GeoMAN[82] 2018 LSTM 注意力 Multi-Stage-Att[83] 2020 LSTM 注意力 CT_CAM[84] 2020 FCN + Bi-GRU 注意力 CA-SFCN[14] 2020 FCN 注意力 RTFN[85] 2021 CNN + LSTM 注意力 LAXCAT[79] 2021 CNN 注意力 MACNN[80] 2021 Multi-scale CNN 注意力 Transformers SAnD[89] 2018 线性Embedding 多头注意力 T2[91] 2021 高斯过程 多头注意力 GTN[93] 2021 线性Embedding 多头注意力 TRANS_tf[90] 2021 时频特征 多头注意力 FMLA[94] 2022 可变形卷积 多头注意力 TFFormer[95] 2023 线性Embedding 多头注意力 自监督注意力 BENDER[71] 2021 Wav2Vec 2.0+ 自监督 多头注意力 TST[15] 2021 线性Embeddin+自监督 多头注意力 TARNet 2022 线性Embedding+自监督 多头注意力 TEST[97] 2023 线性Embedding+自监督 多头注意力
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