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dc.contributor.authorKolková, Andrea
dc.date.accessioned2020-09-21T11:13:16Z
dc.date.available2020-09-21T11:13:16Z
dc.date.issued2020
dc.identifier.citationJournal of Competitiveness. 2020, vol. 12, issue 2, p. 90-105.cs
dc.identifier.issn1804-171X
dc.identifier.issn1804-1728
dc.identifier.urihttp://hdl.handle.net/10084/141797
dc.description.abstractThe accurate forecasting of business variables is a key element for a company's competitiveness which is becoming increasing necessary in this globalized and digitalized environment. Companies are responding to this need by intensifying accuracy requirements for forecasting economic variables. The objective of this article is to verify the correctness of the models predicting revenue in the service sector against 6 precision criteria to determine whether the use of certain criteria may lead to the adoption of particular models to improve competitive forecasting. This article seeks to determine the best accuracy predictors in 32 service areas broken down by NACE. Exponential smoothing models, ARIMA models, BATS models and artificial neural network models were selected for the assessment. Six criteria were chosen to measure accuracy using a group of scale-dependent errors and scaled errors. Services for which the result was ambiguous were subject to complete forecasting, both ex-post and ex-ante. Based on the analysis, the main result of the article is that only two types of services do not achieve the same accuracy results when using other measure criteria. It can therefore be said that for 93.75% of services, an assessment according to one precision parameter would suffice. Thus, a model's competitiveness is not affected by the choice of accuracy.cs
dc.language.isoencs
dc.publisherUniverzita Tomáše Bati ve Zlíně, Fakulta managementu a ekonomikycs
dc.relation.ispartofseriesJournal of Competitivenesscs
dc.relation.urihttp://doi.org/10.7441/joc.2020.02.06cs
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 International License.cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectforecastingcs
dc.subjectETScs
dc.subjectARIMAcs
dc.subjectBATScs
dc.subjectartificial neural networkcs
dc.subjectaccuracycs
dc.subjectcompetitivenesscs
dc.titleThe application of forecasting sales of services to increase business competitivenesscs
dc.typearticlecs
dc.identifier.doi10.7441/joc.2020.02.06
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume12cs
dc.description.issue2cs
dc.description.lastpage105cs
dc.description.firstpage90cs
dc.identifier.wos000546258100007


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