Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/57111
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dc.contributor.authorKobpongkit Navapanen_US
dc.contributor.authorJianxu Liuen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.date.accessioned2018-09-05T03:35:08Z-
dc.date.available2018-09-05T03:35:08Z-
dc.date.issued2017-02-01en_US
dc.identifier.issn1860949Xen_US
dc.identifier.other2-s2.0-85012895340en_US
dc.identifier.other10.1007/978-3-319-50742-2_30en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012895340&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/57111-
dc.description.abstract© Springer International Publishing AG 2017. The levels of cash holding and cash deposit for Thai banks have significantly increased over the past 10 years. This paper aims to forecast cash holding by using cash deposit. For banks, cash holding partially is from the cash deposited. In addition, accurate prediction on the cash holding would provide valuable information and indicators supervising bankers to control the levels of both cash holding and cash deposit effectively. In addition, the empirical relevance of cash holding and cash deposit is examined with three different models; linear model, ARIMA model and state space model. Experimental results with real data sets illustrate that state space model tends be the most accurate model compared to the other two models for prediction.en_US
dc.subjectComputer Scienceen_US
dc.titleForecasting cash holding with cash deposit using time series approachesen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume692en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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