Faster and parameter-free discord search in quasi-periodic time series
Luo, Wei and Gallagher, Marcus 2011, Faster and parameter-free discord search in quasi-periodic time series, in PAKDD 2011 : Advances in Knowledge Discovery and Data Mining : Proceedings of PAKDD 2011, Springer, Heidelberg, Germany, pp. 135-148, doi: 10.1007/978-3-642-20847-8_12.
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Faster and parameter-free discord search in quasi-periodic time series
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.
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