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master's thesis
Planiranje potražnje uz pomoć strojnog učenja
Zagreb: University of Zagreb, Faculty of Transport and Traffic Sciences, 2020. urn:nbn:hr:119:335677
Matijević, Marija
University of Zagreb
Faculty of Transport and Traffic Sciences
Division of Intelligent Transport Systems and Logistics
Department of Transport Logistics

Institutional repository: Faculty of Transport and Traffic Sciences

Cite this document

Matijević, M. (2020). Planiranje potražnje uz pomoć strojnog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Transport and Traffic Sciences. Retrieved from https://urn.nsk.hr/urn:nbn:hr:119:335677

Matijević, Marija. "Planiranje potražnje uz pomoć strojnog učenja." Master's thesis, University of Zagreb, Faculty of Transport and Traffic Sciences, 2020. https://urn.nsk.hr/urn:nbn:hr:119:335677

Matijević, Marija. "Planiranje potražnje uz pomoć strojnog učenja." Master's thesis, University of Zagreb, Faculty of Transport and Traffic Sciences, 2020. https://urn.nsk.hr/urn:nbn:hr:119:335677

Matijević, M. (2020). 'Planiranje potražnje uz pomoć strojnog učenja', Master's thesis, University of Zagreb, Faculty of Transport and Traffic Sciences, accessed 17 February 2025, https://urn.nsk.hr/urn:nbn:hr:119:335677

Matijević M. Planiranje potražnje uz pomoć strojnog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Transport and Traffic Sciences; 2020 [cited 2025 February 17] Available at: https://urn.nsk.hr/urn:nbn:hr:119:335677

M. Matijević, "Planiranje potražnje uz pomoć strojnog učenja", Master's thesis, University of Zagreb, Faculty of Transport and Traffic Sciences, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:119:335677

Metadata
TitlePlaniranje potražnje uz pomoć strojnog učenja
Title (english)Demand Forecasting using Machine Learning
AuthorMarija Matijević
MentorKristijan Rogić (mentor)
Committee memberRatko Stanković (predsjednik povjerenstva)
Committee memberDarko Babić (član povjerenstva)
Committee memberIvona Bajor (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Transport and Traffic Sciences
(Division of Intelligent Transport Systems and Logistics)
(Department of Transport Logistics)
Zagreb
Defense date and country2020-09-24, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Traffic and Transport Technology
Intelligent Transport Systems and Logistics
Abstract
Visoka učinkovitost opskrbnog lanca može se postići primjenom odgovarajućih metoda za dugoročno predviđanje prognoze. Klasične metode prognoze jednostavne su u svome izračunu, ali njihova točnost zna biti na nezavidnoj razini. Stoga su metode strojnog učenja dobra prilika za poboljšanje ovoga polja. Ovaj rad donosi usporedbu prognoze potražnje klasičnom metodom sezonalnog autoregresivnog integriranog pomičnog prosjeka s vanjskim varijablama te metodama strojnog učenja, metoda slučajne šume i metoda umjetne neuronske mreže.
Abstract (english)
High efficiency of the supply chain can be attained by applying the bestperforming methods for long term forecasting. Traditional forecasting methods are easy to implement and compute but their accuracy can be lackluster. Therefore, methods that implement machine learning are a great way to improve this field. This paper compares forecast results using three methods, one of them is a classic method of the autoregressive integrated moving average with external variables, the other two methods are machine learning methods, random forest regression, and artificial neural networks.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:119:335677
Study programmeTitle: Intelligent Transport Systems and Logistics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka prometa (magistar/magistra inženjer/inženjerka prometa)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2020-11-27 13:02:32