Personalised modelling for multiple time-series data prediction

aut.application.number
aut.conference.typePaper Published in Proceedings
aut.publication.placeBerlin
aut.relation.endpage1244
aut.relation.issue1
aut.relation.pages8
aut.relation.startpage1237
aut.relation.volume1
dc.contributor.authorWidiputra, H
dc.contributor.authorPears, R
dc.contributor.authorKasabov, N
dc.contributor.editorKöppen, M
dc.contributor.editorKasabov, N
dc.contributor.editorCoghill, G
dc.date.accessioned2013-02-26T04:30:57Z
dc.date.available2013-02-26T04:30:57Z
dc.date.copyright2008
dc.date.issued2008
dc.description.abstractThe behaviour of multiple stock markets can be described within the framework of complex dynamic systems (CDS). Using a global model with the Kalman Filter we are able to extract the dynamic interaction network (DIN) of these markets. The model was shown to successfully capture interactions between stock markets in the long term. In this study we investigate the effectiveness of two different personalised modelling approaches to multiple stock market prediction. Preliminary results from this study show that the personalised modelling approach when applied to the rate of change of the stock market index is better able to capture recurring trends that tend to occur with stock market data.
dc.identifier.citation15th International Conference on Neural Information Processing of the Asia-Pacific Neural Network Assembly, 2008 held at Sky City Convention Centre, Auckland, New Zealand, 2008-11-25 to 2008-11-28, vol.1(1), pp.1237 - 1244 (8)
dc.identifier.doi10.1007/978-3-642-02490-0_150
dc.identifier.roid7831en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/5179
dc.publisherSpringer-Verlag
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.titlePersonalised modelling for multiple time-series data prediction
dc.typeConference Contribution
pubs.elements-id7413
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
pubs.organisational-data/AUT/Design & Creative Technologies/School of Computing & Mathematical Science
pubs.organisational-data/AUT/PBRF Researchers
pubs.organisational-data/AUT/PBRF Researchers/Design & Creative Technologies PBRF Researchers
pubs.organisational-data/AUT/PBRF Researchers/Design & Creative Technologies PBRF Researchers/DCT C & M Computing
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