@inproceedings{108381b18c6146aea43f3248213a254c,
title = "An online anomalous time series detection algorithm for univariate data streams",
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.",
keywords = "Outlier detection, anomalous time series detection, online algorithms, real time detection",
author = "Huaming Huang and Kishan Mehrotra and Mohan, {Chilukuri K.}",
year = "2013",
doi = "10.1007/978-3-642-38577-3_16",
language = "English (US)",
isbn = "9783642385766",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "151--160",
booktitle = "Recent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings",
note = "26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013 ; Conference date: 17-06-2013 Through 21-06-2013",
}