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master's thesis
Usporedba algoritama za predviđanje cijena kriptovaluta
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. urn:nbn:hr:168:997389
Brkić, Goran
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Brkić, G. (2024). Usporedba algoritama za predviđanje cijena kriptovaluta (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:997389

Brkić, Goran. "Usporedba algoritama za predviđanje cijena kriptovaluta." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:997389

Brkić, Goran. "Usporedba algoritama za predviđanje cijena kriptovaluta." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:997389

Brkić, G. (2024). 'Usporedba algoritama za predviđanje cijena kriptovaluta', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 27 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:997389

Brkić G. Usporedba algoritama za predviđanje cijena kriptovaluta [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2024 [cited 2024 December 27] Available at: https://urn.nsk.hr/urn:nbn:hr:168:997389

G. Brkić, "Usporedba algoritama za predviđanje cijena kriptovaluta", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:168:997389

Metadata
TitleUsporedba algoritama za predviđanje cijena kriptovaluta
Title (english)Comparison of cryptocurrency price prediction algorithms
AuthorGoran Brkić
MentorMartina Antonić (mentor)
Committee memberMartina Antonić (predsjednik povjerenstva)
Committee memberKrešimir Pripužić (član povjerenstva)
Committee memberDragan Jevtić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2024-07-11, Croatia
Abstract
Ovaj rad istražuje primjenu različitih modela strojnog učenja za predviđanje smjera kretanja cijene kriptovaluta. Analizirani su modeli logističke regresije, SVM-a, slučajne šume, XGBoosta i LSTM-a koristeći preciznost, odziv i F1-ocjenu kao mjeru uspješnosti. Analiza je provedena nad kriptovalutnim parovima BTC/USDT, ETH/USDT i LINK/USDT kako bi se dodatno saznalo je li predviđanje kriptovaluta niže tržišne kapitalizacije jednostavnije ili teže. Kroz pregled relevantne literature, tehničke analize cijene, implementaciju pet različitih modela strojnog učenja te analizu dobivenih rezultata pruža se pregled mogućnosti korištenja strojnog učenja za predviđanje kretanja kriptovaluta. Rezultati pokazuju da navedeni modeli pružaju ograničenu prednost u odnosu na nasumično predviđanje smjera te podržavaju teoriju slučajnog hoda. Također, rezultati pokazuju smanjenu sposobnost predviđanja kriptovalutnog para manje tržišne kapitalizacije. Istraživanje pruža korisne smjernice za implementaciju navedenih modela te naglašava potrebu za daljnjim optimiziranjem modela koji je postigao najveću točnost, XGBoost.
Abstract (english)
This paper explores different machine learning models and their ability to predict the direction of price movement for different cryptocurrencies. Analysis was conducted on models of logistic regression, SVM, random forest, XGBoost and LSTM using precision, recall and F1-score as a method of comparison. Analysis was done on cryptocurrency pairs BTC/USDT, ETH/USDT and LINK/USDT to find out whether cryptocurrencies with lower market capitalization are harder to predict. Through a review of relevant literature, technical price analysis, the implementation of five different machine learning models, and the analysis of the obtained results, this paper provides an overview of the possibilities of using machine learning for predicting cryptocurrency movements. The results indicate that these models offer a limited advantage compared to random prediction and support the random walk theory. Additionally, the findings suggest a reduced ability to predict cryptocurrencies with lower market capitalizations. This research offers useful guidelines for implementing the mentioned models and emphasizes the need for further optimization of the model that achieved the highest accuracy, XGBoost.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:997389
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2024-10-12 20:12:05