Fusion imaging-based recurrent capsule classification network for time series

doi:10.11772/j.issn.1001-9081.2022010089

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (3): 692-699.DOI: 10.11772/j.issn.1001-9081.2022010089

Special Issue: 人工智能

• Artificial intelligence • Previous Articles Next Articles

Fusion imaging-based recurrent capsule classification network for time series

Rongjun CHEN¹^,², Xuanhui YAN¹^,²(), Chaocheng YANG¹^,²

^1.College of Computer and Cyberspace Security，Fujian Normal University，Fuzhou Fujian 350117，China
^2.Digital Fujian Internet-of-Things Laboratory of Environmental Monitoring （Fujian Normal University），Fuzhou Fujian 350117，China

Received:2022-01-25 Revised:2022-04-19 Accepted:2022-04-20 Online:2022-04-26 Published:2023-03-10
Contact: Xuanhui YAN
About author:CHEN Rongjun， born in 1996， M. S. candidate. His research interests include deep learning， time series analysis.
YANG Chaocheng， born in 1996， M. S. candidate. His research interests include time series analysis， machine learning.
Supported by:
Guiding Project of Fujian Provincial Science and Technology Department(2020H0011)

面向时间序列的混合图像化循环胶囊分类网络

陈容均¹^,², 严宣辉¹^,²(), 杨超城¹^,²

^1.福建师范大学计算机与网络空间安全学院，福州 350117
^2.数字福建环境监测物联网实验室（福建师范大学），福州 350117

通讯作者: 严宣辉
作者简介:陈容均（1996—），男，福建三明人，硕士研究生，主要研究方向：深度学习、时间序列分析
严宣辉（1968—），男，福建福州人，副教授，博士，主要研究方向：人工智能、深度学习
杨超城（1996—），男，河南周口人，硕士研究生，主要研究方向：时间序列分析、机器学习。
基金资助:
福建省科技厅引导性项目(2020H0011)

Abstract

Abstract:

To address the problem of lack of temporal correlations and spatial location relationships in imaging time series， Fusion-Imaing Recurrent Capsule Neural Network （FIR-Capsnet） for time series was proposed to fuse and extract spatial-temporal information from time series images. Firstly， the multi-level spatial-temporal features of time series images were captured by using Gramian Angular Field （GAF）， Markov Transition Field （MTF） and Recurrence Plot （RP）. Then， the spatial relationships of time series images were learnt by the rotation invariance of capsule neural network and iterative routing algorithm. Finally， the temporal correlations hidden in the time series data were learnt by the gate mechanism of Long-Short Term Memory （LSTM） network. Experimental results show that FIR-Capsnet achieves 15 wins on 30 UCR public datasets and outperforms Fusion-CNN by 7.2 percentage points in classification accuracy on Human Activity Recognition （HAR） dataset， illustrating the advantages of FIR-Capsnet in processing time series data.

Key words: pattern recognition, time series classification, deep learning, imaging time series, recurrent capsule neural network

摘要：

针对时间序列图像化缺少时间关联关系与空间位置关系的问题，提出面向时间序列的混合图像化循环胶囊神经网络（FIR-Capsnet）以融合并提取时间序列图像的时空信息。首先通过格拉姆角场（GAF）、马尔可夫跃迁场（MTF）与重现图（RP）方法捕获时间序列图像的多水平时空特征；然后利用胶囊神经网络的旋转不变性与路由迭代算法学习时间序列图像的空间关系；最后引入长短时记忆（LSTM）网络的门机制学习时间序列数据隐含的时间关联性。实验结果表明，FIR-Capsnet在30个UCR公开数据集上取得15次胜利；并且在人体活动识别（HAR）数据集上相较于Fusion-CNN、FIR-Capsnet的分类准确率提高7.2个百分点，说明了FIR-Capsnet处理时序数据的优势。

关键词: 模式识别, 时间序列分类, 深度学习, 时间序列图像化, 循环胶囊神经网络

CLC Number:

TP391.4

Rongjun CHEN, Xuanhui YAN, Chaocheng YANG. Fusion imaging-based recurrent capsule classification network for time series[J]. Journal of Computer Applications, 2023, 43(3): 692-699.

陈容均, 严宣辉, 杨超城. 面向时间序列的混合图像化循环胶囊分类网络[J]. 《计算机应用》唯一官方网站, 2023, 43(3): 692-699.

Figures/Tables 14

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数据集	类型	训练样本数	测试样本数	时间序列长度	类别数
Computers	Device	250	250	720	2
DistalPhalanxOutline Correct	Image	600	276	80	2
Earthquakes	Sensor	322	139	512	2
ECG5000	ECG	500	4 500	140	5
ElectricDevices	Device	8 926	7 711	96	7
FaceFour	Image	24	88	350	4
FiftyWords	Image	450	455	270	50
InsectWingbeatSound	Sensor	220	1 980	256	11
LargeKitchenAppliances	Device	375	375	720	3
Lightning2	Sensor	60	61	637	2
Mallat	Simulated	55	2 345	1 024	8
Meat	Spectro	60	60	448	3
MiddlePhalanxOutline AgeGroup	Image	400	154	80	3
MiddlePhalanxTW	Image	399	154	80	6
MoteStrain	Sensor	20	1 252	84	2
PhalangesOutlinesCorrect	Image	1 800	858	80	2
ProximalPhalanxOutline Correct	Image	600	291	80	2
ShapeletSim	Simulated	20	180	500	2
ShapesAll	Image	600	600	512	60
SmallKitchenAppliances	Device	375	375	720	3
SonyAIBORobotSurface1	Sensor	20	601	70	2
SonyAIBORobotSurface2	Sensor	27	953	65	2
StarlightCurves	Sensor	1 000	8 236	1 024	3
Strawberry	Spectro	613	370	235	2
Symbols	Image	25	995	398	6
ToeSegmentation1	Motion	40	228	277	2
TwoLeadECG	ECG	23	1 139	82	2
Wine	Spectro	57	54	234	2
WordSynonyms	Image	267	638	270	25
Worms	Motion	181	77	900	5

数据集	类型	训练样本数	测试样本数	时间序列长度	类别数
Computers	Device	250	250	720	2
DistalPhalanxOutline Correct	Image	600	276	80	2
Earthquakes	Sensor	322	139	512	2
ECG5000	ECG	500	4 500	140	5
ElectricDevices	Device	8 926	7 711	96	7
FaceFour	Image	24	88	350	4
FiftyWords	Image	450	455	270	50
InsectWingbeatSound	Sensor	220	1 980	256	11
LargeKitchenAppliances	Device	375	375	720	3
Lightning2	Sensor	60	61	637	2
Mallat	Simulated	55	2 345	1 024	8
Meat	Spectro	60	60	448	3
MiddlePhalanxOutline AgeGroup	Image	400	154	80	3
MiddlePhalanxTW	Image	399	154	80	6
MoteStrain	Sensor	20	1 252	84	2
PhalangesOutlinesCorrect	Image	1 800	858	80	2
ProximalPhalanxOutline Correct	Image	600	291	80	2
ShapeletSim	Simulated	20	180	500	2
ShapesAll	Image	600	600	512	60
SmallKitchenAppliances	Device	375	375	720	3
SonyAIBORobotSurface1	Sensor	20	601	70	2
SonyAIBORobotSurface2	Sensor	27	953	65	2
StarlightCurves	Sensor	1 000	8 236	1 024	3
Strawberry	Spectro	613	370	235	2
Symbols	Image	25	995	398	6
ToeSegmentation1	Motion	40	228	277	2
TwoLeadECG	ECG	23	1 139	82	2
Wine	Spectro	57	54	234	2
WordSynonyms	Image	267	638	270	25
Worms	Motion	181	77	900	5

动作类型	训练样本数	测试样本数
共计	7 352	2 947
走路	1 374	532
上楼	1 286	491
下楼	1 407	537
坐下	1 226	496
站直	986	420
躺下	1 073	471

动作类型	训练样本数	测试样本数
共计	7 352	2 947
走路	1 374	532
上楼	1 286	491
下楼	1 407	537
坐下	1 226	496
站直	986	420
躺下	1 073	471

数据集	Fusion-CNN	GAF-Capsnet	RP-Capsnet	MTF-Capsnet	FIR-Capsnet
Computers	0.630	0.710	0.730	0.720	0.760
DistalPhalanxOutlineCorrect	0.772	0.790	0.772	0.784	0.858
Earthquakes	0.895	0.753	0.828	0.753	0.785
ECG5000	0.823	0.930	0.945	0.946	0.945
ElectricDevices	0.719	0.770	0.809	0.776	0.849
FaceFour	0.826	1.000	1.000	0.957	0.957
FiftyWords	0.470	0.630	0.586	0.646	0.680
InsectWingbeatSound	0.616	0.654	0.666	0.693	0.691
LargeKitchenAppliances	0.507	0.610	0.633	0.640	0.627
Lightning2	0.720	0.800	0.800	0.800	0.800
Mallat	0.960	0.996	1.000	1.000	1.000
Meat	0.833	0.958	0.958	1.000	0.958
MiddlePhalanxOutlineAgeGroup	0.604	0.658	0.667	0.720	0.730
MiddlePhalanxTW	0.559	0.559	0.631	0.523	0.559
MoteStrain	0.914	0.933	0.914	0.965	0.929
PhalangesOutlinesCorrect	0.724	0.780	0.803	0.780	0.800
ProximalPhalanxOutlineCorrect	0.777	0.810	0.804	0.872	0.849
ShapeletSim	0.825	0.650	0.625	0.625	0.700
ShapesAll	0.608	0.713	0.771	0.742	0.833
SmallKitchenAppliances	0.573	0.653	0.726	0.693	0.720
SonyAIBORobotSurface1	0.872	0.952	0.952	0.960	0.968
SonyAIBORobotSurface2	0.948	0.969	0.913	0.949	0.969
StarlightCurves	0.975	0.971	0.962	0.984	0.905
Strawberry	0.954	0.959	0.970	0.975	0.964
Symbols	0.980	0.975	0.956	0.971	0.961
ToeSegmentation1	0.815	0.796	0.796	0.833	0.870
TwoLeadECG	0.991	1.000	0.991	0.991	1.000
Wine	0.869	1.000	1.000	0.826	1.000
WordSynonyms	0.541	0.678	0.552	0.707	0.729
Worms	0.774	0.742	0.871	0.871	0.871
平均准确率	0.748	0.792	0.802	0.802	0.821
方差	0.192	0.182	0.173	0.176	0.156
获胜次数	3	5	8	11	15
平均序值	4.129	3.064	2.645	2.322	1.903
MPCE	0.067	0.056	0.054	0.054	0.050

Fusion imaging-based recurrent capsule classification network for time series

面向时间序列的混合图像化循环胶囊分类网络

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Figures/Tables 14

References 40

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Metrics

网络	各个动作类型准确率						总体准确率
网络	走路	上楼	下楼	坐下	站直	躺下	总体准确率
Fusion-CNN	0.878	0.856	0.865	0.926	0.873	0.918	0.878
GAF-Capsnet	0.933	0.904	0.929	0.953	0.943	0.896	0.933
RP-Capsnet	0.866	0.923	0.946	0.966	0.989	0.921	0.866
MTF-Capsnet	0.909	0.900	0.831	0.994	1.000	0.935	0.929
FIR-Capsnet	0.963	0.942	0.871	1.000	0.998	0.922	0.950

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