MERLIN: Parameter-Free Discovery of Arbitrary Length Anomalies in Massive Time Series Archives
MERLIN: Parameter-Free Discovery of Arbitrary Length Anomalies in Massive Time Series Archives
复制标题
MERLIN：海量时间序列档案中任意长度异常的无参数发现
DOI：
10.1109/icdm50108.2020.00147
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Keogh, Eamonn
中科院分区：
文献类型：
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作者：
Nakamura, Takaaki;Imamura, Makoto;Mercer, Ryan;Keogh, Eamonn
时间序列异常检测一直是一个重要的研究课题。如果有的话，这是一项在物联网蓬勃发展的时代变得越来越重要的任务。虽然在文献中有数百种异常检测方法，但其中一种定义，时间序列不一致，已经成为从业者的竞争和流行选择。时间序列不一致是指时间序列中与最近邻序列相距最远的序列。也许不协和音最吸引人的特点是它们的简单性。不像许多参数加载的方法，discords只需要用户设置一个参数：子序列长度。在这项工作中，我们认为，不和谐的效用是减少敏感性，这一单一的用户选择。这个问题的明显解决方案，计算所有长度的不和谐，然后选择最好的异常（在某种程度上），似乎是计算上站不住脚的。然而，在这项工作中，我们引入MERLIN，一种算法，可以有效地和准确地找到大量的时间序列档案中的所有长度的不一致。
Time series anomaly detection remains a perennially important research topic. If anything, it is a task that has become increasingly important in the burgeoning age of IoT. While there are hundreds of anomaly detection methods in the literature, one definition, time series discords, has emerged as a competitive and popular choice for practitioners. Time series discords are subsequences of a time series that are maximally far away from their nearest neighbors. Perhaps the most attractive feature of discords is their simplicity. Unlike many parameter laden methods, discords require only a single parameter to be set by the user: the subsequence length. In this work we argue that the utility of discords is reduced by sensitivity to this single user choice. The obvious solution to this problem, computing discords of all lengths then selecting the best anomalies (under some measure), seems to be computationally untenable. However, in this work we introduce MERLIN, an algorithm that can efficiently and exactly find discords of all lengths in massive time series archives.
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：
International Conference on Machine Learning and Applications
影响因子：
--
作者：
Iman Vasheghani Farahani;Alex Chien;R. King;M. Kay;Brad Klenz
通讯作者：
Brad Klenz
DOI：
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发表时间：
2015
期刊：
International Conference on Information and Knowledge Management
影响因子：
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作者：
M. Doan;Sutharshan Rajasegarar;Mahsa Salehi;Masud Moshtaghi;C. Leckie
通讯作者：
C. Leckie