特征点分段提取的时间序列模式匹配方法

李正欣; 刘畅; 吴诗辉; 郭建胜

doi:10.13700/j.bh.1001-5965.2021.0546

特征点分段提取的时间序列模式匹配方法

doi: 10.13700/j.bh.1001-5965.2021.0546

空军工程大学装备管理与无人机工程学院，西安 710051

详细信息

通讯作者:
E-mail：lizhengxin_2005@163.com

中图分类号: TP391
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- 文章访问数: 251
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- 被引次数: 0
出版历程
- 收稿日期: 2021-09-13
- 录用日期: 2021-12-10
- 网络出版日期: 2022-02-15
- 整期出版日期: 2023-07-31

Segmentation extraction of feature points for time series pattern matching

Equipment Management and Unmanned Aerial Vehicle Engineering College，Air Force Engineering University，Xi’an 710051，China

More Information

Corresponding author: E-mail：lizhengxin_2005@163.com

摘要

摘要:
常用的时间序列模式匹配方法难以平衡计算复杂度与匹配精度，针对该问题，提出了一种特征点分段提取的时间序列模式匹配方法。提取时间序列每个变量维度上的特征点，降低序列长度；将特征点序列转化为分位点矩阵，利用欧氏距离对分位点矩阵进行相似性度量；在几组时间序列数据集上对所提方法进行分类实验。结果表明：所提方法在降低计算复杂度的同时，获得了较高的匹配精度。
- 时间序列 /
- 模式匹配 /
- 特征提取 /
- 分位点矩阵 /
- 计算复杂度
Abstract:
It is difficult for the common time series pattern matching methods to balance the computational complexity and matching accuracy. To solve this problem, a time series matching method based on segmented extraction of feature points is proposed. Firstly, the feature points on each variable dimension of the time series are extracted and the sequence length is compressed. Then, the quantile matrix is calculated according to the feature sequence, and the similarity of the quantile matrix is measured by Euclidean distance. Finally, the effectiveness of the proposed method is verified on the application data set. Experimental results show that the proposed method can effectively reduce the computational complexity and ensure high matching accuracy.
- time series /
- pattern matching /
- feature extraction /
- quantile matrix /
- computational complexity

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图 1 特征点提取示意图

Figure 1. Schematic diagram of feature point extraction

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图 2 输入序列(ASL_87, boy)

Figure 2. Input sequence (ASL_87, boy)

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图 3 不同方法在ASL数据集上寻找的最相似序列

Figure 3. The most similar sequence found by different methods on ASL dataset

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图 4 滑动窗口因子对匹配精度及计算效率的影响

Figure 4. Influence of sliding window factor on matching accuracy and calculation efficiency

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图 5 滑动窗口因子对特征点提取的影响

Figure 5. Influence of sliding window factor on feature point extraction

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图 6 TD、SFP-ED与DTW计算时间比较

Figure 6. Calculation time of TD、SFP-ED and DTW

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表 1 实验数据集

Table 1. Detail of experimental datasets

数据集	长度	均长	变量个数	样本量	类别
ASL	47~95	59	22	216	8
LP1	15	15	6	88	4
JV	7~29	16	12	640	9
EEG	256	256	64	22	2
WR	128~1918	368	62	44	2
Trace	275	275	1	200	4

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表 2 ASL数据集上的匹配精度

Table 2. Matching accuracy on ASL dataset

k	SVD	DTW	ACM-DTW	TD	PD	SFP-ED
1	0.718	0.977	0.926	0.972	0.579	0.986
5	0.639	0.929	0.870	0.958	0.570	0.947
10	0.570	0.929	0.856	0.906	0.557	0.944

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表 3 6种模式匹配方法在不同准确率下的匹配次数

Table 3. Matching times of 6 pattern matching methods under different accuracy rates

e/%	SVD			DTW			ACM-DTW			TD			PD			SFP-ED
e/%	N=1	N=5	N=10	N=1	N=5	N=10	N=1	N=5	N=10	N=1	N=5	N=10	N=1	N=5	N=10	N=1	N=5	N=10
0	61	20	12	5	0	0	16	9	0	6	0	0	91	33	14	3	0	0
10			19			0			3			0			13			0
20		27	15		0	0		4	0		0	0		25	17		2	0
30			19			1			2			2			28			3
40		34	19		13	0		10	5		4	3		36	18		9	4
50			25			6			5			9			26			1
60		29	26		12	5		10	13		9	8		34	17		6	3
70			7			15			15			7			13			10
80		22	7		14	11		29	17		15	22		23	6		10	7
90			7			30			25			30			12			15
100	155	84	60	211	177	148	200	154	125	210	188	135	125	65	52	213	189	173

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表 4 6组数据集上的匹配精度

Table 4. Accuracy on 6 datasets

数据集	SVD	DTW	ACM-DTW	TD	PD	SFP-ED
ASL	0.718	0.977	0.926	0.972	0.578	0.986
LP1	0.477	0.886	0.909	0.636	0.830	0.921
JV	0.489	0.963	0.759	0.548	0.581	0.958
EEG	0.909	0.727	0.727	1	0.591	0.864
WR	0.977	0.977	0.955	1	1	0.977
Trace		0.995	1	0.930		0.995

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表 5 9个分位点提取结果

Table 5. Nine quantile results

Δ	最小值点	5%	10%	25%	50%	75%	90%	95%	最大值点
1	−0.1111	−0.1052	−0.0975	−0.0528	−0.0512	−0.0506	−0.0460	−0.0414	0.0024
5	−0.1111	−0.1111	−0.1093	−0.0800	−0.0516	−0.0441	−0.0108	0.0024	0.0024
10	−0.1111	−0.1111	−0.1099	−0.0926	−0.0528	−0.0477	−0.0075	0.0024	0.0024

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