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Exact variable-length anomaly detection algorithm for univariate and multivariate time series

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Abstract

The problem of anomaly detection in time series has received a lot of attention in the past two decades. However, existing techniques cannot locate where the anomalies are within anomalous time series, or they require users to provide the length of potential anomalies. To address these limitations, we propose a self-learning online anomaly detection algorithm that automatically identifies anomalous time series, as well as the exact locations where the anomalies occur in the detected time series. In addition, for multivariate time series, it is difficult to detect anomalies due to the following challenges. First, anomalies may occur in only a subset of dimensions (variables). Second, the locations and lengths of anomalous subsequences may be different in different dimensions. Third, some anomalies may look normal in each individual dimension but different with combinations of dimensions. To mitigate these problems, we introduce a multivariate anomaly detection algorithm which detects anomalies and identifies the dimensions and locations of the anomalous subsequences. We evaluate our approaches on several real-world datasets, including two CPU manufacturing data from Intel. We demonstrate that our approach can successfully detect the correct anomalies without requiring any prior knowledge about the data.

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Notes

  1. We use the whole time series to demonstrate the idea, but our approach also works in the scenario when points of time series come in a streaming fashion.

  2. Reflow soldering: https://en.wikipedia.org/wiki/Reflow_soldering.

  3. http://www.eigenvector.com/data/Etch/index.html.

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Authors and Affiliations

  1. George Mason University, Fairfax, USA

    Xing Wang & Jessica Lin

  2. Intel Corporation, Chandler, AZ, USA

    Nital Patel & Martin Braun

Authors
  1. Xing Wang
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  2. Jessica Lin
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  3. Nital Patel
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  4. Martin Braun
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Correspondence to Jessica Lin.

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Communicated by Eamonn Keogh.

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Wang, X., Lin, J., Patel, N. et al. Exact variable-length anomaly detection algorithm for univariate and multivariate time series. Data Min Knowl Disc 32, 1806–1844 (2018). https://doi.org/10.1007/s10618-018-0569-7

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