prikaz prve stranice dokumenta Sustav za predviđanje kretanja vrijednosti kriptovaluta temeljen na dubokom učenju
No public access
master's thesis
Sustav za predviđanje kretanja vrijednosti kriptovaluta temeljen na dubokom učenju
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:636298
Vujnović, Zvonimir
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Vujnović, Z. (2021). Sustav za predviđanje kretanja vrijednosti kriptovaluta temeljen na dubokom učenju (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:636298

Vujnović, Zvonimir. "Sustav za predviđanje kretanja vrijednosti kriptovaluta temeljen na dubokom učenju." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:636298

Vujnović, Zvonimir. "Sustav za predviđanje kretanja vrijednosti kriptovaluta temeljen na dubokom učenju." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:636298

Vujnović, Z. (2021). 'Sustav za predviđanje kretanja vrijednosti kriptovaluta temeljen na dubokom učenju', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 14 January 2025, https://urn.nsk.hr/urn:nbn:hr:168:636298

Vujnović Z. Sustav za predviđanje kretanja vrijednosti kriptovaluta temeljen na dubokom učenju [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2025 January 14] Available at: https://urn.nsk.hr/urn:nbn:hr:168:636298

Z. Vujnović, "Sustav za predviđanje kretanja vrijednosti kriptovaluta temeljen na dubokom učenju", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:636298

Metadata
TitleSustav za predviđanje kretanja vrijednosti kriptovaluta temeljen na dubokom učenju
Title (english)System for Prediction of Criptocurency Value Changes Based on Deep Learning
AuthorZvonimir Vujnović
MentorTomislav Hrkać (mentor)
Committee memberTomislav Hrkać (predsjednik povjerenstva)
Committee memberZoran Kalafatić (član povjerenstva)
Committee memberJan Šnajder (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-02, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovom radu opisane su kriptovalute i povratne neuronske mreže. Kriptovalute su "digitalne" valute. Opisano je što su i kako su izvedene te koja je njihova svrha. Nakon toga opisano je što su povratne neuronske mreže, problemi koje rješavaju, arhitekture koje postoje i njihove varijacije. U zadnjem poglavlju opisani su modeli pomoću kojih se predviđa cijena kriptovaluta. Model se sastoji od LSTM ćelija. To su posebne vrste RNN ćelija koje imaju bolje dugoročno pamćenje od same RNN ćelije. Iako je model vrlo jednostavan, dobro uči i interpretira podatke, ali nije dovoljno pouzdan za davati u potpunosti točne predikcije jer je tržište kriptovaluta jako volatilno.
Abstract (english)
This paper describes cryptocurrencies and recurrent neural networks. Cryptocurrencies are “digital” currencies. It describes what they are, how they were made and what their purpose is. It is then described what recurrent neural networks are, the problems they solve, the architectures that exist, and their variations. The last chapter describes the models used to predict the price of cryptocurrencies. The model consists of LSTM cells. These are special types of RNN cells that have better long-term memory than the RNN cell itself. Although the model is very simple, it learns and interprets the data well, but it is not reliable enough to give completely accurate predictions because the cryptocurrency market is very volatile.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:636298
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
Access conditionsClosed access
Terms of useLicence In copyright icon
Public note
Created on2023-01-13 16:31:01