@inproceedings{f211642e8f1b40bebc26514ecbcf203f,
title = "Detection of anomalous time series based on multiple distance measures",
abstract = "Automatic detection of anomalous series is an important task and several approaches have been suggested using a single detection measure. We propose a multi-measure based approach and compare it with existing methods. To select a short list of effective measures we perform extensive evaluations for several combinations of proposed measures. Our results show that the proposed algorithm is able to detect a variety of anomalies in datasets from different domains. Our approach outperforms existing methods that use single measures, which detect only some types of anomalies.",
keywords = "Anomalous time series detection, Distance measures, Outlier detection, Rank-based methods, Time series",
author = "Huaming Huang and Mehrotra, {Kishan G.} and Mohan, {Chilukuri K.}",
year = "2013",
language = "English (US)",
isbn = "9781622769728",
series = "28th International Conference on Computers and Their Applications 2013, CATA 2013",
pages = "147--152",
booktitle = "28th International Conference on Computers and Their Applications 2013, CATA 2013",
note = "28th International Conference on Computers and Their Applications 2013, CATA 2013 ; Conference date: 04-03-2013 Through 06-03-2013",
}