基于深度学习的时间序列分类研究综述

任利强; 贾舒宜; 王海鹏; 王子玲

doi:10.11999/JEIT231222

基于深度学习的时间序列分类研究综述

doi: 10.11999/JEIT231222 cstr: 32379.14.JEIT231222

海军航空大学烟台 264001

基金项目: 国家自然科学基金(62076249)，山东省自然科学基金(ZR2020MF154)

详细信息

作者简介:
任利强：男，博士生，研究方向为信息融合、飞行数据智能处理

贾舒宜：女，副教授，研究方向为信息融合

王海鹏：男，教授，研究方向为信息融合

王子玲：女，副教授，研究方向为信息融合

通讯作者:
贾舒宜　ganlingxian@sina.com

中图分类号: TN911.7; TN99; TP183
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- 被引次数: 0
出版历程
- 收稿日期: 2023-11-03
- 修回日期: 2023-12-21
- 网络出版日期: 2023-12-27
- 刊出日期: 2024-08-30

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

Naval Aviation University, Yantai 264001, China

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

摘要

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4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。
- 深度学习 /
- 时间序列 /
- 神经网络 /
- 分类 /
- 综述
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

HTML全文

图 1 基于网络结构和应用领域的深度学习时间序列分类体系结构

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图 2 用于单变量时间序列分类的多层感知

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图 3 t-LeNet 时间序列特定版本网络架构

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图 4 两层循环神经网络的架构

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图 5 自注意力机制

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图 6 多头注意力模块

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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]	2017	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]	2018	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]	2020	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]	2023	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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