Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/58575
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dc.contributor.authorTanarat Rattanadamrongaksornen_US
dc.contributor.authorDuangthip Sirikanchanaraken_US
dc.contributor.authorJirakom Sirisrisakulchaien_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.date.accessioned2018-09-05T04:26:25Z-
dc.date.available2018-09-05T04:26:25Z-
dc.date.issued2018-01-01en_US
dc.identifier.issn16113349en_US
dc.identifier.issn03029743en_US
dc.identifier.other2-s2.0-85043980484en_US
dc.identifier.other10.1007/978-3-319-75429-1_28en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85043980484&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/58575-
dc.description.abstract© 2018, Springer International Publishing AG, part of Springer Nature. In this study, the non-parametric Inferential Model or IM with the entropy-based random set has been proposed for the investigation of financial data in the two statistical domains i.e. estimation and prediction. The samples from five financial markets were chosen for representing the different types of financial assets to make a conclusion about this new framework. We found that the Inferential Model performed equally well compared with the traditional method but was more robust so that it might be more appropriate for some specific uses.en_US
dc.subjectComputer Scienceen_US
dc.subjectMathematicsen_US
dc.titleEstimating and Predicting Financial Series by Entropy-Based Inferential Modelen_US
dc.typeBook Seriesen_US
article.title.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
article.volume10758 LNAIen_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsBank of Thailanden_US
Appears in Collections:CMUL: Journal Articles

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