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Title:Uporaba genetskega programiranja za napoved cen kriptovalut : magistrsko delo
Authors:ID Heric, Tilen (Author)
ID Ravber, Miha (Mentor) More about this mentor... New window
Files:.pdf MAG_Heric_Tilen_2025.pdf (4,55 MB)
MD5: 609E7D121641EC66149702DF94ABF0F8
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Odkar so se pojavile kriptovalute in hitro za tem občutek hitrega zaslužka, se je rodilo zanimanje za napoved njihovih cen. Zaradi nepredvidljivosti in visoke volatilnosti gibanja cen tradicionalne metode ne zadoščajo pri ustvarjanju zanesljivih napovedi. Namen magistrskega dela je razvoj napovednega modela, ki bo znal samostojno napovedati ceno kriptovalute naslednjega dne. Cilj napovednega modela je čim natančnejša napoved za več kriptovalut. Model temelji na genetskem programiranju z uporabo simbolične regresije. Vhodni podatki algoritma so zgodovinski podatki in tehnični kazalniki. Z metodo iskanja po mreži smo optimizirali parametre genetskega algoritma. Rezultati napovedovanja kažejo, da je genetsko programiranje učinkovito pri napovedovanju cen kriptovalut, zato smo preizkusili, kako se obnese pri energentih, saj so ti nekoliko manj volatilni. Pri energentih se je model obnesel še bolj učinkovito. Zaključimo lahko, da je naša metoda primerno orodje za obravnavo problemov z visoko volatilnostjo in kompleksnostjo. Naloga je primer praktične uporabe naprednih evolucijskih algoritmov za reševanje realnih problemov in ponuja osnovo za nadaljnje raziskave na področju napovedovanja časovnih vrst.
Keywords:genetsko programiranje, napoved cen, kriptovalute, energenti
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[T. Heric]
Year of publishing:2025
Number of pages:1 spletni vir (1 datoteka PDF (VIII, 50 str.))
PID:20.500.12556/DKUM-95043 New window
UDC:004.421:336.74(043.2)
COBISS.SI-ID:259168003 New window
Publication date in DKUM:15.10.2025
Views:158
Downloads:56
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:03.09.2025

Secondary language

Language:English
Title:Use of genetic programming for cryptocurrency prices prediction
Abstract:Since the emergence of cryptocurrencies and the accompanying promise of quick profits, interest in forecasting their prices has grown rapidly. Due to the unpredictability and high volatility of price movements, traditional methods often fail to provide reliable predictions. The purpose of this thesis is to develop a predictive model capable of independently forecasting the next-day price of a cryptocurrency. The goal of the model is to deliver accurate predictions across multiple cryptocurrencies. The model is based on genetic programming using symbolic regression. The algorithm uses historical data and technical indicators as input features. We optimized the genetic algorithm parameters using grid search. The forecasting results demonstrate that genetic programming is effective in predicting cryptocurrency prices. Therefore, we also tested the model on energy commodities, which tend to be less volatile. The model performed even better in this domain. We conclude that our method is a suitable tool for addressing problems characterized by high volatility and complexity. This thesis represents a practical application of advanced evolutionary algorithms to real-world forecasting problems and provides a solid foundation for future research in time series prediction.
Keywords:genetic programming, price prediction, cryptocurrencies, energy commodities


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