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dc.contributor.authorKanchana Chokethawornen_US
dc.contributor.authorChukiat Chaiboonsrien_US
dc.contributor.authorSatawat Wannapanen_US
dc.description.abstract© 2019 IOP Publishing Ltd. All rights reserved. This paper is conducted to substantially do the two alternatives between the traditional statistical methods such as ARMA and HW models, and artificial intelligence (AI) contains KNN and ELM, respectively. To scope the main object of the paper, SET indexes are collected as the main financial variable, which are 5,472 daily observed days during 9 September 1997 to 11 June 2018. Technically, the cross-entropy (CE) analysis, MSE and RMSE calculations are computationally employed to clarify the resolution of the two computations. The empirical results state that the AI prediction can be a substitution replacing the traditional estimations, and this can strongly confirm that machine learning (ML) algorithms are continuously interested, and they are recently becoming a powerful tool for modern econometric forecasting.en_US
dc.subjectPhysics and Astronomyen_US
dc.titleAlternative prediction methods in the stock exchanges of Thailanden_US
dc.typeConference Proceedingen_US
article.title.sourcetitleJournal of Physics: Conference Seriesen_US
article.volume1324en_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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