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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Somsak Chanaim | en_US |
| dc.contributor.author | Wilawan Srichaikul | en_US |
| dc.contributor.author | Chongkolnee Rungruang | en_US |
| dc.contributor.author | Songsak Sriboonchitta | en_US |
| dc.date.accessioned | 2019-08-05T04:39:41Z | - |
| dc.date.available | 2019-08-05T04:39:41Z | - |
| dc.date.issued | 2019-01-01 | en_US |
| dc.identifier.issn | 16860209 | en_US |
| dc.identifier.other | 2-s2.0-85068484033 | en_US |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85068484033&origin=inward | en_US |
| dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/65694 | - |
| dc.description.abstract | © 2019 by the Mathematical Association of Thailand. All rights reserved. The GDP is very important for measure the economics growth in country, in this paper use the relevance vector machine(RVM) to predict the GDP in some ASIAN country (Thailand, Malaysia and Singapore) and compare with the autoregressive model (AR(p)). From the result show that RVM dominate the AP(p) model by measuring the error (MAE, MAPE, MSE and RMSE) from both training data and validation data. | en_US |
| dc.subject | Mathematics | en_US |
| dc.title | Forecasting GDP in ASIAN countries using relevant vector machines | en_US |
| dc.type | Journal | en_US |
| article.title.sourcetitle | Thai Journal of Mathematics | en_US |
| article.volume | 17 | en_US |
| article.stream.affiliations | Prince of Songkla University | en_US |
| article.stream.affiliations | Chiang Mai University | en_US |
| Appears in Collections: | CMUL: Journal Articles | |
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