Correlation Set Discovery on Time-Series Data

Amagata, Daichi; Hara, Takahiro

doi:https://doi.org/10.1007/978-3-030-27618-8_21

Correlation Set Discovery on Time-Series Data

Amagata, Daichi; Hara, Takahiro

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11707, 275-290

Number of Access:660（2025-08-15 10:59 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/92852

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Title	Correlation Set Discovery on Time-Series Data
Creator	NRID 1000040770649 1000040770649 researchmap permalink amgt amgt Scopus Author ID 55811167300 55811167300 CreatorName Amagata, Daichi
Creator	NRID 1000020294043 1000020294043 ORCID 0000-0003-4807-3156 0000-0003-4807-3156 researchmap permalink hara-osakau hara-osakau Scopus Author ID 7404213547 7404213547 CreatorName Hara, Takahiro
Subject	Correlation set
Subject	Time-series
Description	This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-27618-8_21.
Abstract	Time-series data analysis is essential in many modern applications, such as financial markets, sensor networks, and data centers, and correlation discovery is a core technique for the analysis. In this paper, we address a novel problem that computes a k-sized time-series dataset where the minimum Pearson correlation of any two time-series in the set is maximized. This problem discovers a group of time-series, which are highly correlated with each other, from a given time-series dataset without any prior knowledge, thus helps many analytical applications. We show that this problem is NP-hard, and design an approximate heuristic solution that provides a high quality result with fast response time. Extensive experiments on real and synthetic datasets verify the efficiency, effectiveness, and scalability of our solution.
Conference Information	Conference Name International Conference on Database and Expert Systems Applications Conference Sequence 30 Conference Date August 26th to August 29th, 2019 Conference Place Linz Conference Country
Publisher	Springer
Source Title	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume (Issue)	11707
Page	275-290
Date of Issued	2019-08-06
Language	English
Handle URL	https://hdl.handle.net/11094/92852
Relation.isVersionOf	DOI (Publisher Version) https://doi.org/10.1007/978-3-030-27618-8_21
Relation.isPartOf	ISBN 9783030276171
Relation.isPartOf	ISBN 9783030276188
Access Rights	open access
CopyRight	© 2019, Springer Nature Switzerland AG.
oaire:version	Accepted Manuscript
Category	会議発表論文 Conference Paper

Resource Type	conference paper
Local Type	Conference Paper
DCMI Type	Text
DCTERMS.bibliographicCitation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2019, 11707, p. 275-290
DC.title	Correlation Set Discovery on Time-Series Data
DC.creator	Amagata, Daichi
DC.creator	Hara, Takahiro
DC.publisher	Springer
DC.language' scheme='DCTERMS.RFC1766	English
DC.type' scheme='DCTERMS.DCMIType	Text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2019-08-06
DC.identifier	https://doi.org/10.1007/978-3-030-27618-8_21
DC.subject	Correlation set
DC.subject	Time-series
DC.description	This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-27618-8_21.
DCTERMS.abstract	Time-series data analysis is essential in many modern applications, such as financial markets, sensor networks, and data centers, and correlation discovery is a core technique for the analysis. In this paper, we address a novel problem that computes a k-sized time-series dataset where the minimum Pearson correlation of any two time-series in the set is maximized. This problem discovers a group of time-series, which are highly correlated with each other, from a given time-series dataset without any prior knowledge, thus helps many analytical applications. We show that this problem is NP-hard, and design an approximate heuristic solution that provides a high quality result with fast response time. Extensive experiments on real and synthetic datasets verify the efficiency, effectiveness, and scalability of our solution.
DC.rights	© 2019, Springer Nature Switzerland AG.
DC.format	92852
citation_title	Correlation Set Discovery on Time-Series Data
citation_author	Amagata, Daichi
citation_author	Hara, Takahiro
citation_publisher	Springer
citation_language	English
citation_date	2019-08-06
citation_journal_title	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
citation_volume	11707
citation_firstpage	275
citation_lastpage	290
citation_doi	https://doi.org/10.1007/978-3-030-27618-8_21
citation_public_url	https://hdl.handle.net/11094/92852

Correlation Set Discovery on Time-Series Data

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