Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/34338
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dc.contributor.authorUžupytė, Rūta-
dc.contributor.authorKrilavičius, Tomas-
dc.coverage.spatialLT-
dc.date.accessioned2017-04-19T06:56:31Z-
dc.date.available2017-04-19T06:56:31Z-
dc.date.issued2014-
dc.identifier.issn18225934-
dc.identifier.otherVDU02-000016696-
dc.identifier.urihttps://eltalpykla.vdu.lt/1/34338-
dc.description.abstractReliable methodology for service orders prediction can significantly improve the quality of business strategy. It is very important to identify the seasonal behavior in order data to correctly predict customer demand and make appropriate business decisions. There are several methods to model and forecast time series with seasonal pattern. This paper compares seasonal naive, Holt – Winters seasonal, SARIMA and neural networks methods in order to evaluate their performance in prediction of the future values of time series that consist of the monthly orders in a small IT companyen
dc.description.sponsorshipBaltijos pažangių technologijų institutas-
dc.description.sponsorshipBaltijos pažangių technologijų institutas, Vilnius-
dc.description.sponsorshipInformatikos fakultetas-
dc.description.sponsorshipVytauto Didžiojo universitetas-
dc.format.extentp. 68-73-
dc.language.isoen-
dc.relation.ispartofECT-2014 : Electrical and control technologies : proceedings of the 9th international conference on electrical and control technologies, May 8-9, 2014, Kaunas, Lithuania. Kaunas : Technologija, 9 (2014)-
dc.rightsSutarties data 2015-01-07, nr. B000076, laisvai prieinamas internetelt_LT
dc.subjectOntologijoslt
dc.subjectDaugiakalbiai dokumentailt
dc.subjectOrders predictionen
dc.subjectTime seriesen
dc.subject.classificationStraipsnis recenzuojamoje Lietuvos tarptautinės konferencijos medžiagoje / Article in peer-reviewed Lithuanian international conference proceedings (P1e)-
dc.subject.otherInformatika / Informatics (N009)-
dc.titleOrders prediction for small IT companyen
dc.typeresearch article-
dcterms.bibliographicCitation12-
dc.date.updated2020-04-02T11:13Z-
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local.typeP-
item.grantfulltextopen-
item.fulltextWith Fulltext-
crisitem.author.deptMatematikos ir statistikos katedra-
crisitem.author.deptTaikomosios informatikos katedra-
Appears in Collections:3. Konferencijų medžiaga / Conference materials
Universiteto mokslo publikacijos / University Research Publications
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