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master's thesis
Predviđanje cijena kriptovaluta korištenjem analize vremenskih serija
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:004593
Matić, Toni
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Matić, T. (2023). Predviđanje cijena kriptovaluta korištenjem analize vremenskih serija (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:004593

Matić, Toni. "Predviđanje cijena kriptovaluta korištenjem analize vremenskih serija." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:004593

Matić, Toni. "Predviđanje cijena kriptovaluta korištenjem analize vremenskih serija." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:004593

Matić, T. (2023). 'Predviđanje cijena kriptovaluta korištenjem analize vremenskih serija', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 21 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:004593

Matić T. Predviđanje cijena kriptovaluta korištenjem analize vremenskih serija [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 June 21] Available at: https://urn.nsk.hr/urn:nbn:hr:168:004593

T. Matić, "Predviđanje cijena kriptovaluta korištenjem analize vremenskih serija", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:004593

Metadata
TitlePredviđanje cijena kriptovaluta korištenjem analize vremenskih serija
Title (english)Cryptocurrency Prediction Using Time Series Analysis
AuthorToni Matić
MentorMarin Šilić (mentor)
Committee memberMarin Šilić (predsjednik povjerenstva)
Committee memberGoran Delač (član povjerenstva)
Committee memberKlemo Vladimir (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-04, Croatia
Abstract
Kriptovalute su digitalni oblik valuta kojima upravlja decentralizirano tržište. Ulagačima je cilj predvidjeti cijene kriptovaluta te tako profitirati. Cijene je kriptovaluta moguće gledati kao vremensku seriju, pa ih tako i analizirati ih pomoću metoda i alata za analizu vremenskih serija. Analiza vremenskih serija područje je dubinske analize podataka koje analizira serije podataka, a cilj joj je pronaći zanimljive obrasce te ih onda iskoristiti za druge korisne zadatke. Cilj je ovoga rada analizom vremenskih serija predvidjeti buduće cijene kriptovaluta metodama strojnog učenja. Korišteni su modeli eksponencijalnog zaglađivanja, ARIMA, Prophet i povratna neuronska mreža. Osim samih cijena, analizirani su i vanjski događaji koji su utjecali na promjene cijena kriptovaluta, a to je učinjeno analizom sentimenta objava s Twittera.
Abstract (english)
Cryptocurrencies are a digital form of currency managed by a decentralized market. The goal of many investors is to predict the prices of cryptocurrencies in advance and, using that, make a profit. Cryptocurrency prices can be viewed as a time series, and thus analyzed using time series analysis methods and tools. Time series analysis is a field of data mining that explores a series of data with the goal of finding interesting patterns in it and using them for other valuable tasks. The aim of this thesis is to predict the future prices of cryptocurrencies by analyzing time series using machine learning methods. Exponential smoothing, ARIMA, Prophet and recurrent neural networks were used. In addition to analysing cryptocurrency prices themselves, external events that influenced changes in the prices were also analyzed by using the sentiments of published Twitter posts.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:004593
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
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Created on2023-07-13 10:20:52