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Title:Razvoj napovednega modela multivariatnih časovnih vrst uporabniških storitev : diplomsko delo
Authors:ID Pečečnik, Sandi (Author)
ID Lukač, Niko (Mentor) More about this mentor... New window
ID Bizjak, Marko (Comentor)
Files:.pdf UN_Pececnik_Sandi_2023.pdf (1,66 MB)
MD5: 1FC9D51CDA10D988B625FE95F6EEAFD8
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V sklopu diplomskega dela predstavimo več nevronskih mrež, ki jih optimiziramo, pri čemer raziščemo ustrezne arhitekture, metrike, funkcije in druge pomembne lastnosti za uporabo v napovednih modelih multivariantnih časovnih vrst. Raziščemo najpomembnejše probleme povezane z razvojem napovednih nevronskih mrež. Naslovimo reševanje treh specifičnih realnih problemov, za reševanje katerih smo predlagali arhitekture nevronskih mrež. Izdelali smo še skalabilno spletno aplikacijo, ki omogoča enostavnejšo uporabo naučenih modelov nevronskih mrež.
Keywords:časovne vrste, nevronske mreže, globoko učenje, storitve, arhitekture globokega učenja
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[S. Pečečnik]
Year of publishing:2023
Number of pages:1 spletni vir (1 datoteka PDF (XIII, 66 f.))
PID:20.500.12556/DKUM-85352 New window
UDC:004.032.26:004.2(043.2)
COBISS.SI-ID:171593219 New window
Publication date in DKUM:05.10.2023
Views:620
Downloads:95
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:28.08.2023

Secondary language

Language:English
Title:Development of prediction model for multivariate time series of user services
Abstract:As part of the thesis, we propose several neural networks that we optimize, exploring appropriate architectures, metrics, functions and other important properties for use in multivariate time series prediction models. We investigate the most important problems related to the development of predictive neural networks. We address the solution of three specific real problems, for the solution of which we proposed neural network architectures. We have also created a scalable web application that enables easier use of learned neural network models.
Keywords:time series, neural networks, deep learning, services, deep learning architectures


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