Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/102058
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dc.contributor.authorKuizinienė, Dovilė-
dc.contributor.authorMackutė-Varoneckienė, Aušra-
dc.contributor.authorKrilavičius, Tomas-
dc.coverage.spatialDE-
dc.date.accessioned2019-12-03T16:49:09Z-
dc.date.available2019-12-03T16:49:09Z-
dc.date.issued2019-
dc.identifier.issn16130073-
dc.identifier.otherVDU02-000062014-
dc.identifier.urihttps://www.vdu.lt/cris/bitstream/20.500.12259/102058/2/ISSN1613-0073_2019_V_2470.PG_70-73.pdf-
dc.identifier.urihttps://hdl.handle.net/20.500.12259/102058-
dc.description.abstractCryptocurrency are difficult to forecast due to it’s globality and availability to everyone and every time. There is no Friday or Holidays effect, seasonality, market news and other aspects, which influence the course direction. It is the phenomena of the market and it is useful to spread forecast methods research to find out the best fitting model for this phenomenon. In this paper is presented short-term forecast of five different cryptocurrencies (Bitcoin, BitcoinCash, Ethereum, Litecoin, Ripple). Forecast methods split in two groups: 1) real value (ARIMA and SVR models) 2) volatility (GARCH and SVR models). The model’s suitability is evaluated by RMSE and MAE. The best results for real value forecast were achieved using ARIMA, for volatility forecast - SVR. In further research it would be useful to analyze methods variety of Artificial Neural Networks and others connected models’ modificationsen
dc.description.sponsorshipBaltijos pažangių technologijų institutas, Vilnius-
dc.description.sponsorshipInformatikos fakultetas-
dc.description.sponsorshipTaikomosios informatikos katedra-
dc.description.sponsorshipVytauto Didžiojo universitetas-
dc.format.extentp. 70-73-
dc.language.isoen-
dc.relation.ispartofCEUR Workshop proceedings [electronic resource]: IVUS 2019, International conference on information technologies, Kaunas, Lithuania, 25 April, 2019. Aachen : CEUR-WS, 2019, Vol. 2470-
dc.relation.isreferencedbyScopus-
dc.rightshttps://creativecommons.org/licenses/by/4.0/-
dc.subjectCryptocurrencyen
dc.subjectBitcoinen
dc.subjectARIMAen
dc.subjectGARCHen
dc.subjectSVRen
dc.subjectValue forecasten
dc.subject.otherInformatika / Informatics (N009)-
dc.titleCryptocurrencies short-term forecast: application of ARIMA, GARCH and SVR modelsen
dc.typeStraipsnis konferencijos medžiagoje kitose duomenų bazėse / Article in conference proceedings in other databases (P1c)-
dcterms.bibliographicCitation15-
dc.date.updated2019-12-04T10:27Z-
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local.typeP-
item.grantfulltextopen-
item.fulltextWith Fulltext-
crisitem.author.deptInformatikos fakultetas-
crisitem.author.deptInformatikos fakultetas-
crisitem.author.deptTaikomosios informatikos katedra-
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications
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