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Title:Kratkoročno napovedovanje lokalnih vremenskih parametrov s konvolucijsko nevronsko mrežo : magistrsko delo
Authors:ID Razpotnik, Aljaž (Author)
ID Strnad, Damjan (Mentor) More about this mentor... New window
ID Kohek, Štefan (Comentor)
Files:.pdf MAG_Razpotnik_Aljaz_2019.pdf (9,60 MB)
MD5: 801343E956580DC2BD4793CF73C3043C
PID: 20.500.12556/dkum/0da64d3f-e1fb-4939-b288-455b1bc96d8a
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrskem delu se ukvarjamo z napovedovanjem časovnih vrst. Časovno vrsto predstavljajo podatki vremenskih parametrov večjega števila krajev, ki smo jih pridobili iz spletnega arhiva Agencije Republike Slovenije za okolje. Za napovedovanje vremenskih parametrov izbranega kraja uporabimo pretekle podatke samega kraja in njegove okolice ter z njimi učimo napovedne modele ARIMAX, CART, GRU in kombinirani model CNN-LSTM. Pri kombiniranem modelu upoštevamo geografske soodvisnosti uporabljenih krajev, ki jih preslikamo v matriko. Iz predstavljenih rezultatov je razvidno, da sta najboljša modela za napovedovanje vremenskih parametrov GRU in CNN-LSTM.
Keywords:vremenski parametri, časovna vrsta, napovedovanje, nevronska mreža, regresijsko drevo
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[A. Razpotnik]
Year of publishing:2019
Number of pages:X, 53 str.
PID:20.500.12556/DKUM-75421 New window
UDC:[519.2:004.8]:551.509(043.2)
COBISS.SI-ID:22877718 New window
NUK URN:URN:SI:UM:DK:AQWEPULN
Publication date in DKUM:10.12.2019
Views:1685
Downloads:221
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
:
RAZPOTNIK, Aljaž, 2019, Kratkoročno napovedovanje lokalnih vremenskih parametrov s konvolucijsko nevronsko mrežo : magistrsko delo [online]. Master’s thesis. Maribor : A. Razpotnik. [Accessed 10 April 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=75421
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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:10.11.2019

Secondary language

Language:English
Title:Short-Term prediction of local weather parameters with convolutional neural network
Abstract:The master thesis deals with time series forecasting. The time series is presented by the data of weather parameters of a large number of places, which are obtained from the web archive of the Slovenian Environment Agency. In order to forecast the weather parameters of a specific place, past data of that place and its environment are used for teaching the forecast models ARIMAX, CART, GRU and the combined CNN-LSTM model. The combined model requires the consideration of geographic interdependencies between places, which are mapped into the matrix. The results demonstrate that the most effective models for forecasting the weather parameters are GRU and CNN-LSTM.
Keywords:weather parameters, time series, forecasting, neural network regression tree


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