Abstract
Anomalous patterns or discords are just the kind of outliers in time series. In this paper, we present a new approach for time series discord discovery which is based on cluster-based outlier detection. In this approach, first, subsequence candidates are extracted from the time series using a segmentation method, then these candidates are transformed into the same length and are input for an appropriate clustering algorithm, and finally, we identify discords by using a measure suggested in the cluster-based outlier detection method given by He et al. 2003. The experimental results show that our approach is much more efficient than the HOTSAX algorithm in detecting time series discords while the anomalous patterns discovered by the two methods perfectly match with each other.
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Acknowledgement
We are grateful to Prof. Eamonn Keogh for his kindly providing all the test datasets used in this work.
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Kha, N.H., Anh, D.T. (2015). From Cluster-Based Outlier Detection to Time Series Discord Discovery. In: Li, XL., Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D. (eds) Trends and Applications in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science(), vol 9441. Springer, Cham. https://doi.org/10.1007/978-3-319-25660-3_2
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