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A Piecewise Aggregate pattern representation approach for anomaly detection in time series

机译：时间序列异常检测的分段聚合模式表示方法

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摘要

In the area of time series representation, the Piecewise Aggregate Approximation (PAA) method has established itself quite visibly resulting in a number of useful results. However, the PAA technique usually leads to some losses of information. In order to overcome this issue, we propose a representation approach called Piecewise Aggregate Pattern Representations (PAPR). In the PAPR method, the range of values assumed in the temporal segment is divided into several regions with equal probability. In the sequel, some statistics of the regions, such as the number, the mean and the variance of points falling within each region, are determined. A matrix (pattern) containing all these statistical characteristics is constructed to represent the corresponding segment of the time series. We incorporate the PAPR method into anomaly detection by computing the similarity of patterns and using a Random Walk (RW) model, as a classifier, to determine the similarity values. Finally, the connectivity and anomaly ranks of patterns are obtained with the use of the RW model. The overall anomaly detection approach is referred to as PAPR-RW. Experimental studies are reported for synthetic data sets and two publicly available data sets: electrocardiograms (ECGs) data and the video surveillance data. Compared with the PM-based method, the PAPR-RW approach exhibits a higher level of robustness and detects anomalies more accurately. (C) 2017 Elsevier B.V. All rights reserved.
机译：在时间序列表示领域，分段汇总逼近（PAA）方法已经很明显地确立了自己的地位，从而产生了许多有用的结果。但是，PAA技术通常会导致某些信息丢失。为了克服此问题，我们提出了一种称为分段聚合模式表示（PAPR）的表示方法。在PAPR方法中，在时间段中假定的值的范围以相等的概率分为几个区域。在续集中，确定了区域的一些统计信息，例如每个区域内点的数量，均值和方差。构造包含所有这些统计特征的矩阵（模式）以表示时间序列的相应段。我们通过计算模式的相似度并使用随机游走（RW）模型作为分类器，将PAPR方法整合到异常检测中，以确定相似度值。最后，使用RW模型获得模式的连通性和异常等级。整体异常检测方法称为PAPR-RW。报告了针对合成数据集和两个公共可用数据集的实验研究：心电图（ECG）数据和视频监视数据。与基于PM的方法相比，PAPR-RW方法具有更高的鲁棒性，并且可以更准确地检测异常。 （C）2017 Elsevier B.V.保留所有权利。

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  • 来源
    《Knowledge-Based Systems》 |2017年第1期|29-39|共11页
  • 作者

    Ren Huorong; Liu Mingming; Li Zhiwu; Pedrycz Witold;

  • 作者单位

    Xidian Univ, Sch Electromech Engn, Xian 710071, Shaanxi, Peoples R China|Minist Educ, Key Lab Elect Equipment Struct Design, Xian 710071, Shaanxi, Peoples R China;

    Xidian Univ, Sch Electromech Engn, Xian 710071, Shaanxi, Peoples R China|Minist Educ, Key Lab Elect Equipment Struct Design, Xian 710071, Shaanxi, Peoples R China;

    Xidian Univ, Sch Electromech Engn, Xian 710071, Shaanxi, Peoples R China|Macau Univ Sci & Technol, Inst Syst Engn, Taipa 999078, Macau, Peoples R China;

    Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 2V4, Canada|King Abdulaziz Univ, Dept Elect & Comp Engn, Fac Engn, Jeddah 21589, Saudi Arabia|Polish Acad Sci, Syst Res Inst, PL-00716 Warsaw, Poland;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Time series; Anomaly detection; Pattern representation; Random walk;

    机译：时间序列;异常检测;模式表示;随机游走;

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