File(s) under permanent embargo
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.
History
Event
Pacific-Asia Conference on Knowledge Discovery and Data Mining (15th : 2011 : Shenzhen, China)Pagination
135 - 148Publisher
SpringerLocation
Shenzhen, ChinaPlace of publication
Heidelberg, GermanyPublisher DOI
Start date
2011-05-24End date
2011-05-27ISBN-13
9783642283192Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2011, SpringerEditor/Contributor(s)
J Huang, L Cao, J SrivastavaTitle of proceedings
PAKDD 2011 : Advances in Knowledge Discovery and Data Mining : Proceedings of PAKDD 2011Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC