Time Series Anomaly Detection from a Markov Chain Perspective
Time Series Anomaly Detection from a Markov Chain Perspective
复制标题
从马尔可夫链角度进行时间序列异常检测
DOI：
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发表时间：
2019
期刊：
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通讯作者：
Brad Klenz
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文献类型：
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作者：
Iman Vasheghani Farahani;Alex Chien;R. King;M. Kay;Brad Klenz
本文介绍了一种基于模式的异常检测问题的新方法，该方法旨在寻找行为与时间序列中其他部分不同的片段（而不是在经典的异常检测问题中寻找单个数据点）。研究这一问题的一个重要动机是发现数据点在正常范围内但却产生异常模式的异常。为此，通过对训练数据集的重叠子序列进行聚类并通过马尔可夫链分析其顺序来发现数据的正常特征。训练后的模型用于评估测试数据集与基线行为的匹配程度。所设计的异常检测框架能够发现流数据（在线）和存储数据（离线）中的异常模式。通过将该方法应用于来自不同领域的三个数据集来评估该方法的性能：医疗数据集（心电图），公用事业使用数据集和纽约市出租车需求数据集。在医学数据中检测到的异常与文献研究的结果一致。一位领域专家证实了公用事业使用数据结果的准确性，而纽约市出租车需求数据的异常与美国主要假日有关。
This paper introduces a new method for the pattern-wise anomaly detection problem, which aims to find segments whose behaviors are different from the rest of the segments in the time series (as opposed to finding a single data-point in classic anomaly detection problems). An important motivation for studying this problem is to find anomalies whose data-points are within the normal range but they create an unusual pattern. To this end, normal characteristics of the data are found by clustering the overlapping subsequences of the training dataset and analyzing their orders by Markov chains. The trained model is used to assess how well the testing dataset suits the baseline behavior. The designed anomaly detection framework is capable of discovering unusual patterns in both streaming data (online) and stored data (offline). The performance of the methodology is evaluated by applying it to three datasets from different fields: a medical dataset (electrocardiogram), a utility usage dataset, and a New York City taxi demand dataset. The detected anomaly in the medical data agrees with the results of the studies in the literature. A domain expert confirmed the accuracy of the results for the utility usage data, and the anomalies of the New York City taxi demand data referred to major US holidays.