Associative topic models with numerical time series

Cited 6 time in webofscience Cited 5 time in scopus
  • Hit : 636
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorPark, Sungraeko
dc.contributor.authorLee, Wonsungko
dc.contributor.authorMoon, Il-Chulko
dc.date.accessioned2015-11-20T07:39:55Z-
dc.date.available2015-11-20T07:39:55Z-
dc.date.created2015-08-07-
dc.date.created2015-08-07-
dc.date.created2015-08-07-
dc.date.issued2015-09-
dc.identifier.citationINFORMATION PROCESSING & MANAGEMENT, v.51, no.5, pp.737 - 755-
dc.identifier.issn0306-4573-
dc.identifier.urihttp://hdl.handle.net/10203/200807-
dc.description.abstractA series of events generates multiple types of time series data, such as numeric and text data over time, and the variations of the data types capture the events from different angles. This paper aims to integrate the analyses on such numerical and text time-series data influenced by common events with a single model to better understand the events. Specifically, we present a topic model, called an associative topic model (ATM), which finds the soft cluster of time-series text data guided by time-series numerical value. The identified clusters are represented as word distributions per clusters, and these word distributions indicate what the corresponding events were. We applied ATM to financial indexes and president approval rates. First, ATM identifies topics associated with the characteristics of time-series data from the multiple types of data. Second, ATM predicts numerical time-series data with a higher level of accuracy than does the iterative model, Which is supported by lower mean squared errors.-
dc.languageEnglish-
dc.publisherELSEVIER SCI LTD-
dc.titleAssociative topic models with numerical time series-
dc.typeArticle-
dc.identifier.wosid000359504900011-
dc.identifier.scopusid2-s2.0-84934763419-
dc.type.rimsART-
dc.citation.volume51-
dc.citation.issue5-
dc.citation.beginningpage737-
dc.citation.endingpage755-
dc.citation.publicationnameINFORMATION PROCESSING & MANAGEMENT-
dc.identifier.doi10.1016/j.ipm.2015.06.007-
dc.contributor.localauthorMoon, Il-Chul-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorTime series analysis-
dc.subject.keywordAuthorTopic models-
dc.subject.keywordAuthorText mining-
Appears in Collection
IE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 6 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0