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master's thesis
Kratkoročno predviđanje cijena kriptovaluta upotrebom dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:574284
Bernat, Andrea
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Bernat, A. (2021). Kratkoročno predviđanje cijena kriptovaluta upotrebom dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:574284

Bernat, Andrea. "Kratkoročno predviđanje cijena kriptovaluta upotrebom dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:574284

Bernat, Andrea. "Kratkoročno predviđanje cijena kriptovaluta upotrebom dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:574284

Bernat, A. (2021). 'Kratkoročno predviđanje cijena kriptovaluta upotrebom dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 25 August 2024, https://urn.nsk.hr/urn:nbn:hr:168:574284

Bernat A. Kratkoročno predviđanje cijena kriptovaluta upotrebom dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 August 25] Available at: https://urn.nsk.hr/urn:nbn:hr:168:574284

A. Bernat, "Kratkoročno predviđanje cijena kriptovaluta upotrebom dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:574284

Metadata
TitleKratkoročno predviđanje cijena kriptovaluta upotrebom dubokog učenja
Title (english)Short-term Forecasting of Cryptocurrency Prices by Deep Learning
AuthorAndrea Bernat
MentorVladimir Čeperić (mentor)
Committee memberVladimir Čeperić (predsjednik povjerenstva)
Committee memberSiniša Šegvić (član povjerenstva)
Committee memberAnte Đerek (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-14, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj rad predstavlja primjenu modela povratne neuronske mreže s mogućnošću kratkoročnih i dugotrajnih ovisnosti (engl. \textit{Long Short Memmory}, LSTM) na problemu predviđanja kretanja cijena kriptovaluta. LSTM mreža je snažan model za modeliranje podataka vremenskih serija jer ima definirano unutarnje stanje kako bi se mogli pratiti već viđeni podaci. Predviđanje cijena kriptovaluta smatra se vrlo izazovnim zadatkom zbog svoje kaotične prirode. U ovome radu pristupili smo tom problemu i pokazali kako odabrati značajke koje utječu na promjenu kretanja cijena kriptovaluta. Detaljno smo opisali arhitekturu modela dubokog učenja i pokazali kako pristupiti odabiru hiperparametara modela. Ovaj rad mogao bi pomoći financijskim stručnjacima u analizi i predviđanju kretanja cijena kriptovaluti, no pošto je predviđanje tržišta vrlo nestabilno pa time i rizično treba provesti realan sustav ulaganja.
Abstract (english)
This paper presents an example of a recurrent neural network model with the possibility of short-term and long-term dependencies (Long Short Memmory, LSTM) on the problems of forecasting the price movement of cryptocurrency. The LSTM network is a powerful model for modeling time series data because it has a defined internal state so that already seen data can be tracked. Predicting the price of cryptocurrency is considered a very challenging task, due to its chaotic nature. In this paper, we approached this problem and showed how to choose features that affect the change in the price movement of cryptocurrency. We described in detail the architecture of the deep learning model and showed how to approach hyperparameter tunning. This paper could help financial experts in analyzing and forecasting the price movement of cryptocurrencies. However, the market forecast is very volatile and risky over time so we need to implement a realistic investment system.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:574284
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 on2023-01-13 11:48:15