Učenje s prenosom znanja z uporabo rekurentnih nevronskih mrež za namen napovedovanja cen živil : magistrsko delo

Janković, Jana

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Title:	Učenje s prenosom znanja z uporabo rekurentnih nevronskih mrež za namen napovedovanja cen živil : magistrsko delo
Authors:	ID Janković, Jana (Author) ID Vrbančič, Grega (Mentor) More about this mentor...
Files:	MAG_Jankovic_Jana_2024.pdf (4,19 MB) MD5: 65A0B4FD0D47AF2EEEE060468A51176C
Language:	Slovenian
Work type:	Master's thesis/paper
Typology:	2.09 - Master's Thesis
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	Živila so osnovne dobrine z velikim vplivom na gospodarsko in družbeno stabilnost, zato je natančno napovedovanje njihovih cen ključno. Modeli globokega učenja lahko prepoznajo kompleksne vzorce v časovnih vrstah, kot so zgodovinske cene živil. V tej raziskavi smo eksperimentalno primerjali konvencionalni pristop učenja in učenje s prenosom znanja v rekurentnih nevronskih mrežah za napovedovanje cen. Po iskanju optimalnih hiperparametrov smo modele naučili nad podatki, uporabili prenos znanja in ovrednotili oba pristopa. Na podlagi pridobljenih rezultatov smo ugotovili, da učenje s prenosom znanja bistveno pospeši proces učenja, vendar na račun slabše napovedne uspešnosti. Kljub temu pa rezultati magistrskega dela prispevajo k razumevanju, kdaj, zakaj in v kakšnih primerih je uporaba učenja s prenosom znanja smiselna izbira.
Keywords:	napovedovanje cen živil, učenje s prenosom znanja, RNN, analiza časovnih vrst.
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	[J. Janković]
Year of publishing:	2024
Number of pages:	1 spletni vir (1 datoteka PDF (XI, 73 str.))
PID:	20.500.12556/DKUM-91245
UDC:	004.85.032.26(043.2)
COBISS.SI-ID:	225867523
Publication date in DKUM:	15.01.2025
Views:	262
Downloads:	83
Metadata:
Categories:	KTFMB - FERI
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Licences

License:	CC BY 4.0, Creative Commons Attribution 4.0 International

Link:	http://creativecommons.org/licenses/by/4.0/
Description:	This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

Language:	English
Title:	Utilization of transfer learning with recurrent neural networks for grocery price forecasting
Abstract:	Food is a fundamental commodity with a significant impact on economic and social stability, making accurate price forecasting essential. Deep learning models can identify complex patterns in time series, such as historical food prices. In this study, we experimentally compared the conventional learning approach with transfer learning in recurrent neural networks for price forecasting. After identifying optimal hyperparameters, we trained the models, applied transfer learning, and evaluated both approaches. Based on the obtained results, we found that transfer learning significantly accelerates the learning process, though at the cost of predictive performance. Nevertheless, the results of this master’s thesis contribute to understanding when, why, and in what scenarios transfer learning is a sensible choice.
Keywords:	food price prediction, transfer learning, RNN, time series analysis.

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