Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/34338
Type of publication: Article in Clarivate Analytics Web of Science or Scopus DB conference proceedings (P1a);Straipsnis konferencijos medžiagoje Clarivate Analytics Web of Science ar/ir Scopus (P1a)
Field of Science: Informatika (N009);Computer science (N009)
Author(s): Krilavičius, Tomas;Užupytė, Rūta
Title: Orders prediction for small IT company
Is part of: ECT-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)
Extent: p. 68-73
Date: 2014
Keywords: Daugiakalbiai dokumentai;Time series;Ontologijos;Orders prediction
Abstract: Reliable 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 company
Internet: https://eltalpykla.vdu.lt/1/34338
https://hdl.handle.net/20.500.12259/34338
Affiliation(s): Informatikos fakultetas
Baltijos pažangių technologijų institutas
Baltijos pažangių technologijų institutas, Vilnius
Vytauto Didžiojo universitetas
Appears in Collections:3. Konferencijų medžiaga / Conference materials
Universiteto mokslo publikacijos / University Research Publications

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