Faster and parameter-free discord search in quasi-periodic time series
Contribuinte(s) |
Huang, Joshua Zhexue Cao, Longbing Srivastava, Jaideep |
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Data(s) |
01/01/2011
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Resumo |
Time series discord has proven to be a useful concept for time-series anomaly identification. To search for discords, various algorithms have been developed. Most of these algorithms rely on pre-building an index (such as a trie) for subsequences. Users of these algorithms are typically required to choose optimal values for word-length and/or alphabet-size parameters of the index, which are not intuitive. In this paper, we propose an algorithm to directly search for the top-K discords, without the requirement of building an index or tuning external parameters. The algorithm exploits quasi-periodicity present in many time series. For quasi-periodic time series, the algorithm gains significant speedup by reducing the number of calls to the distance function. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Springer |
Relação |
http://dro.deakin.edu.au/eserv/DU:30052496/luo-fasterandparameter-2011.pdf http://dx.doi.org/10.1007/978-3-642-20847-8_12 |
Direitos |
2011, Springer |
Palavras-Chave | #time series discord #minimax search #time series data mining #anomaly detection #periodic time series |
Tipo |
Conference Paper |