Abstract
We address the online anomalous time series detection problem among a set of series, combining three simple distance measures. This approach, akin to control charts, makes it easy to determine when a series begins to differ from other series. Empirical evidence shows that this novel online anomalous time series detection algorithm performs very well, while being efficient in terms of time complexity, when compared to approaches previously discussed in the literature. © 2013 Springer-Verlag.
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CITATION STYLE
Huang, H., Mehrotra, K., & Mohan, C. K. (2013). An online anomalous time series detection algorithm for univariate data streams. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7906 LNAI, pp. 151–160). https://doi.org/10.1007/978-3-642-38577-3_16
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