[1]李云霞,姚建国,万定生,等.一种水文时间序列异常模式检测方法研究[J].计算机技术与发展,2019,29(07):159-163.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 032]
　LI Yun-xia,YAO Jian-guo,WAN Ding-sheng,et al.An Anomaly Pattern Detection Method for Hydrological Time Series[J].,2019,29(07):159-163.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 032]
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一种水文时间序列异常模式检测方法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年07期
页码:
159-163
栏目:
应用开发研究
出版日期:
2019-07-10

文章信息/Info

Title:
An Anomaly Pattern Detection Method for Hydrological Time Series
文章编号:
1673-629X(2019)07-0159-05
作者:
李云霞1 ; 姚建国2 ; 万定生1 ; 赵 群1
1. 河海大学 计算机与信息学院,江苏 南京 211100; 2. 淮河水利委员会水文局,安徽 蚌埠 233001
Author(s):
LI Yun-xia 1 ; YAO Jian-guo 2 ; WAN Ding-sheng 1 ; ZHAO Qun 1
1. School of Computer and Information,Hohai University,Nanjing 211100,China;2. Bureau of Hydrology of Huaihe River Commission,Bengbu 233001,China
关键词:
时间序列; 分段线性表示; 层次聚类; 异常因子; 异常模式
Keywords:
time series; piecewise linear representation; hierarchical clustering; outlier factor; anomaly pattern
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 07. 032
摘要:
时间序列数据是一类常见的多维复杂类型数据,它客观记录了观测系统随时间次序而变化的、在各观测时刻点的重要信息。 时间序列数据具有海量性、高维性、复杂性等特点,直接对原始水文时间序列进行异常检测需要花费大量的时间,因此提出一种基于两阶段的水文时间序列异常检测方法。 该方法通过分段线性表示方法对原始时间序列进行表示, 提取子序列的斜率,极值差和均值三个特征值来表示原始时间序列。 第一阶段在每个子序列为一个三元组的基础上用层次聚类算法对数据进行聚类,得到聚类结果。 第二阶段基于聚类结果计算每一类的异常因子,根据异常因子判定异常模式。 为验证该方法的有效性,采用龙门站的实测数据和人工合成数据进行实验检测,取得了较好的效果。
Abstract:
Time series data is a kind of common multi-dimensional complex data,which objectively records the important information of the observation system changing with time order at each observation point. Time series data is characterized by massiveness,high dimensionality,complexity and so on,and it takes a lot of time to conduct anomaly detection in the original hydrological time series. Therefore, we present an anomaly detection method for hydrological time series based on the two-stage. The original time series is represented by piecewise linear representation method,the slope,the extreme difference and the mean of the subsequence are extracted to express the original sequence. In the first stage,the hierarchical clustering algorithm is used to cluster the data on the basis subsequence,and the clustering result is obtained. In the second stage,based on clustering results the outlier factors of each type are calculated to detect anomaly patterns. In order to demonstrate the effectiveness of this method,the actual dataset of Longmen Station as well as artificial dataset are used for testing,and better results are obtained.

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更新日期/Last Update: 2019-07-10