A novel evolving clustering algorithm with polynomial regression for chaotic time-series prediction

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aut.relation.endpage121
aut.relation.startpage114
aut.relation.volume5864
dc.contributor.authorWidiputra, H
dc.contributor.authorKho, H
dc.contributor.authorLukas
dc.contributor.authorPears, R
dc.contributor.authorKasabov, N
dc.contributor.editorLeung, CS
dc.contributor.editorLee, M
dc.contributor.editorChan, JH
dc.date.accessioned2013-02-26T04:42:48Z
dc.date.available2013-02-26T04:42:48Z
dc.date.copyright2009
dc.date.issued2009
dc.description.abstractTime-series prediction has been a very well researched topic in recent studies. Some popular approaches to this problem are the traditional statistical methods e.g. multiple linear regression and moving average, and neural network with the Multi Layer Perceptron which has shown its supremacy in time-series prediction. In this study, we used a different approach based on evolving clustering algorithm with polynomial regressions to find repeating local patterns in a time-series data. To illustrate chaotic time-series data we have taken into account the use of stock price data from Indonesian stock exchange market and currency exchange rate data. In addition, we have also conducted a benchmark test using the Mackey Glass data set. Results showed that the algorithm offers a considerably high accuracy in time-series prediction and could also reveal repeating patterns of movement from the past.
dc.identifier.citationNeural Information Processing: Lecture Notes in Computer Science Volume 5864, 2009, pp 114-121
dc.identifier.doi10.1007/978-3-642-10684-2_13
dc.identifier.issn0302-9743
dc.identifier.roid14243en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/5194
dc.publisherSpringer Berlin Heidelberg
dc.rightsAn author may self-archive an author-created version of his/her article on his/her own website and or in his/her institutional repository. He/she may also deposit this version on his/her funder’s or funder’s designated repository at the funder’s request or as a result of a legal obligation, provided it is not made publicly available until 12 months after official publication. He/ she may not use the publisher's PDF version, which is posted on www.springerlink.com, for the purpose of self-archiving or deposit. Furthermore, the author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at www.springerlink.com”. (Please also see Publisher’s Version and Citation).
dc.rights.accessrightsOpenAccess
dc.subjectEvolving clustering algorithm
dc.subjectPolynomial regression
dc.subjectChaotic time-series data
dc.subjectDiscovery
dc.titleA novel evolving clustering algorithm with polynomial regression for chaotic time-series prediction
dc.typeConference Contribution
pubs.elements-id7410
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
pubs.organisational-data/AUT/Design & Creative Technologies/School of Computing & Mathematical Science
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pubs.organisational-data/AUT/PBRF Researchers/Design & Creative Technologies PBRF Researchers/DCT C & M Computing
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