Exemplar learning for extremely efficient anomaly detection in real-valued time series

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• 作者：Michael Jones ; Daniel Nikovski ; Makoto Imamura…
• 关键词：Anomaly detection ; Time series ; Exemplar learning
• 刊名：Data Mining and Knowledge Discovery
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：30
• 期：6
• 页码：1427-1454
• 全文大小：4,431 KB
• 刊物类别：Computer Science
• 刊物主题：Data Mining and Knowledge Discovery
  Computing Methodologies
  Artificial Intelligence and Robotics
  Statistics
  Statistics for Engineering, Physics, Computer Science, Chemistry and Geosciences
  Information Storage and Retrieval
  
• 出版者：Springer Netherlands
• ISSN：1573-756X
• 卷排序：30

文摘

We investigate algorithms for efficiently detecting anomalies in real-valued one-dimensional time series. Past work has shown that a simple brute force algorithm that uses as an anomaly score the Euclidean distance between nearest neighbors of subsequences from a testing time series and a training time series is one of the most effective anomaly detectors. We investigate a very efficient implementation of this method and show that it is still too slow for most real world applications. Next, we present a new method based on summarizing the training time series with a small set of exemplars. The exemplars we use are feature vectors that capture both the high frequency and low frequency information in sets of similar subsequences of the time series. We show that this exemplar-based method is both much faster than the efficient brute force method as well as a prediction-based method and also handles a wider range of anomalies. We compare our algorithm across a large variety of publicly available time series and encourage others to do the same. Our exemplar-based algorithm is able to process time series in minutes that would take other methods days to process.