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Title:Analiza trga kriptovalut s postopki slepega ločevanja izvorov : magistrsko delo
Authors:Mikolič, Jan (Author)
Holobar, Aleš (Mentor) More about this mentor... New window
Files:.pdf MAG_Mikolic_Jan_2020.pdf (1,78 MB)
MD5: E1AAB835A9A649F5BE07C1D7B2FE2D95
Work type:Master's thesis/paper (mb22)
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrskem delu izvedemo analizo trga kriptovalut z metodami slepega ločevanja izvorov. Osredotočimo se na algoritma FastICA in SOBI. Preizkusimo različne vrednosti vhodnih parametrov in stroškovnih funkcij. Ugotovimo, da je algoritem SOBI s številom zakasnitev 400 primernejši, saj izkorišča časovno strukturo zgodovinskih cen kriptovalut. Na podlagi mešalnega modela kriptovalute gručimo v skupine, na katere vplivajo podobni dejavniki. Predstavimo model za napovedovanje cen kriptovalut na podlagi izračunanih neodvisnih komponent. Zaključimo z ugotovitvijo, da napovedovanje cen kriptovalut zgolj na podlagi zgodovinskih podatkov o cenah najverjetneje ni možno ne glede na napovedovalni model in predhodne transformacije.
Keywords:kriptovalute, analiza neodvisnih komponent, slepo ločevanje izvorov, napovedovanje časovnih vrst, FastICA, SOBI
Year of publishing:2020
Place of performance:Maribor
Publisher:[J. Mikolič]
Number of pages:VIII, 49 str.
COBISS_ID:23070998 New window
Categories:KTFMB - FERI
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License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
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:16.01.2020

Secondary language

Title:Cryptocurrency Market Analysis with Blind Source Separation Algorithms
Abstract:In this master's thesis we perform cryptocurrency market analysis with blind source separation algorithms. We focus on algorithms FastICA and SOBI. Different input parameters and cost functions are tested. Algorithm SOBI with number of lags 400 proves to be the best choice as it exploits the time coherence of the cryptocurrency historical price data. Given the mixing model, we perform clustering and identify groups of cryptocurrencies which are under the influence of similar factors or sources. Further on, a forecasting model, based on calculated independent components, is presented. We conclude that cryptocurrency time series forecasting based on historical price data alone is most likely not possible, regardless of forecasting model or previous transformations used.
Keywords:cryptocurrencies, independent component analysis, blind source separation, time series forecasting, FastICA, SOBI


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