Semantic discord: Finding unusual local patterns for time series

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Abstract

Finding anomalous subsequence in a long time series is a very important but difficult problem. Existing state-of-the-art methods have been focusing on searching for the subsequence that is the most dissimilar to the rest of the subsequences; however, they do not take into account the background patterns that contain the anomalous candidates. As a result, such approaches are likely to miss local anomalies. We introduce a new definition named semantic discord, which incorporates the context information from larger subsequences containing the anomaly candidates. We propose an efficient algorithm with a derived lower bound that is up to 3 orders of magnitude faster than the brute force algorithm in real world data. We demonstrate that our method significantly outperforms the state-of-the-art methods in locating anomalies by extensive experiments. We further explain the interpretability of semantic discord.

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Zhang, L., Gao, Y., & Lin, J. (2020). Semantic discord: Finding unusual local patterns for time series. In Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020 (pp. 136–144). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611976236.16

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