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Conference Paper: Patterns discovery based on time-series decomposition

TitlePatterns discovery based on time-series decomposition
Authors
Issue Date2001
PublisherSpringer.
Citation
5th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2001), Hong Kong, China, 16-18 April 2001. In Advances in Knowledge Discovery and Data Mining: 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001: Proceedings, 2001, p. 336-347 How to Cite?
Abstract© Springer-Verlag Berlin Heidelberg 2001. Complete or partial periodicity search in time-series databases is an interesting data mining problem. Most previous studies on finding periodic or partial periodic patterns focused on data struc- tures and computing issues. Analysis of long-term or short-term trends over different time windows is a great interest. This paper presents a new approach to discovery of periodic patterns from time-series with trends based on time-series decomposition. First, we decompose time series into three components, seasonal, trend and noise. Second, with an existing partial periodicity search algorithm, we search either par- tial periodic patterns from trends without seasonal component or partial periodic patterns for seasonal components. Different patterns from any combination of the three decomposed time-series can be found using this approach. Examples show that our approach is more flexible and suitable to mine periodic patterns from time-series with trends than the previous reported methods.
Persistent Identifierhttp://hdl.handle.net/10722/276698
ISBN
ISSN
2023 SCImago Journal Rankings: 0.606
Series/Report no.Lecture Notes in Computer Science ; 2035

 

DC FieldValueLanguage
dc.contributor.authorYu, Jeffrey Xu-
dc.contributor.authorNg, Michael K.-
dc.contributor.authorHuang, Joshua Zhexue-
dc.date.accessioned2019-09-18T08:34:23Z-
dc.date.available2019-09-18T08:34:23Z-
dc.date.issued2001-
dc.identifier.citation5th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2001), Hong Kong, China, 16-18 April 2001. In Advances in Knowledge Discovery and Data Mining: 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001: Proceedings, 2001, p. 336-347-
dc.identifier.isbn9783540419105-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/276698-
dc.description.abstract© Springer-Verlag Berlin Heidelberg 2001. Complete or partial periodicity search in time-series databases is an interesting data mining problem. Most previous studies on finding periodic or partial periodic patterns focused on data struc- tures and computing issues. Analysis of long-term or short-term trends over different time windows is a great interest. This paper presents a new approach to discovery of periodic patterns from time-series with trends based on time-series decomposition. First, we decompose time series into three components, seasonal, trend and noise. Second, with an existing partial periodicity search algorithm, we search either par- tial periodic patterns from trends without seasonal component or partial periodic patterns for seasonal components. Different patterns from any combination of the three decomposed time-series can be found using this approach. Examples show that our approach is more flexible and suitable to mine periodic patterns from time-series with trends than the previous reported methods.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofAdvances in Knowledge Discovery and Data Mining: 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001: Proceedings-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 2035-
dc.titlePatterns discovery based on time-series decomposition-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/3-540-45357-1_36-
dc.identifier.scopuseid_2-s2.0-84942927438-
dc.identifier.spage336-
dc.identifier.epage347-
dc.publisher.placeBerlin-
dc.identifier.issnl0302-9743-

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