一种基于模板匹配与隶属度解析的时序异常快速检测方法

doi:10.16180/j.cnki.issn1007-7820.2022.06.001

Abstract

Abstract:

Traditional data analysis techniques have some problems in processing large-scale medical data detection, such as high time consumption and weak anti-jamming ability. In order to solve these problems, this study presents a fast detection and analysis method for abnormal state of time series data which uses template matching and membership degree analysis techniques. This method uses TSTKS algorithm and sliding window theory to realize rapid detection of multiple mutation points in time series data, extract continuous multi-window fluctuation characteristics, construct a normalized fluctuation vector of time series data, and perform abnormal state detection and analysis on large-scale disease signals. Simulation data and EEG signal analysis show that this method is a relatively fast and accurate method for big data analysis and detection.

Key words: mutation point detection, big data analytics, anomaly detection, sliding window, time series, fluctuating vector, template matching, membership analysis

CLC Number:

TP311

GONG Hanxin,QI Jinpeng,KONG Fanshu,ZHU Junjun,CAO Yitong. A Fast Time Series Anomaly Detection Method Based on Template Matching and Membership Analysis[J].Electronic Science and Technology, 2022, 35(6): 1-5.

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References 19

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窗口宽度W	时耗/ms	误差/%	命中率/%
2⁶	76.6	33.21	62.31
2⁷	34.9	30.69	76.93
2⁸	17.6	26.31	86.56
2⁹	9.1	32.22	87.83
2¹⁰	6.7	31.68	78.57

算法	时耗/ms	准确率/%	查全率/%
隶属度分析法	16.85	88.92	92.33
相关系数法	23.47	85.69	87.58
Manhattan距离法	14.23	76.82	68.35

A Fast Time Series Anomaly Detection Method Based on Template Matching and Membership Analysis

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