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DC Field | Value | Language |
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dc.contributor.author | Teerawut Teetranont | en_US |
dc.contributor.author | Woraphon Yamaka | en_US |
dc.contributor.author | Songsak Sriboonchitta | en_US |
dc.date.accessioned | 2018-09-05T04:26:03Z | - |
dc.date.available | 2018-09-05T04:26:03Z | - |
dc.date.issued | 2018-01-01 | en_US |
dc.identifier.issn | 1860949X | en_US |
dc.identifier.other | 2-s2.0-85037821108 | en_US |
dc.identifier.other | 10.1007/978-3-319-70942-0_43 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85037821108&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/58539 | - |
dc.description.abstract | © Springer International Publishing AG 2018. This paper employ VAR model to analyse and investigate the relationship among oil, gold, and rubber prices. A convex combination approach is proposed to obtain appropriate value of the interval data in VAR model. The construction of interval VAR model based on the convex combination method for the analysis of their forecast performance are also introduced and discussed via the simulation study, as well as comparing the performance with conventional center method. To illustrate the usefulness of the proposed model, an empirical application on a weekly sample of commodity price is provided. The results show the performance of our proposed model and also provide some relationship between commodity prices. | en_US |
dc.subject | Computer Science | en_US |
dc.title | Generalize weighted in interval data for fitting a vector autoregressive model | en_US |
dc.type | Book Series | en_US |
article.title.sourcetitle | Studies in Computational Intelligence | en_US |
article.volume | 753 | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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