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Title:Proučevanje zunanjih dejavnikov pri napovedovanju cene kriptovalut s strojnim učenjem : diplomsko delo
Authors:ID Cvetko, Jakob (Author)
ID Karakatič, Sašo (Mentor) More about this mentor... New window
Files:.pdf UN_Cvetko_Jakob_2023.pdf (2,08 MB)
MD5: BA5581D45BBAD863E8EBA2D255AAB27F
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Zmožnost napovedovanja gibanja cene finančnih instrumentov predstavlja priložnost za visoke zaslužke. Eni izmed tehnični pristopov, ki se na področju finančnega trgovanja že dalj časa uspešno uporabljajo, so metode strojnega učenja. V diplomski nalogi smo se ukvarjali z napovedovanjem cene kriptovalute Bitcoin. Modeliranje smo začeli s pridobivanjem raznih podatkov, povezanih s ceno kriptovalute, in nato z algoritmom XGBoost izdelali napovedni model. Razumevanje napovedi je ključnega pomena, zato smo uporabili razlagalni algoritem SHAP, s katerim smo dobili globlji vpogled v napovedni model. Izkazalo se je, da imajo podatki, neposredno vezani na ceno kriptovalute, največjo vlogo pri napovedi, temu pa sledi indeks strahu in pohlepa.
Keywords:kriptovalute, strojno učenje, XGBoost, napovedovanje časovnih vrst, SHAP
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[J. Cvetko]
Year of publishing:2023
Number of pages:1 spletni vir (1 datoteka PDF (VII, 33 f.))
PID:20.500.12556/DKUM-84001 New window
UDC:004.85:[004.7:336.74](043.2)
COBISS.SI-ID:155101699 New window
Publication date in DKUM:07.06.2023
Views:951
Downloads:132
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
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:27.03.2023

Secondary language

Language:English
Title:External factors in predicting cryptocurrency price with machine learning
Abstract:Ability to forecast the price of financial instruments has a lot of potential for monetary gains through investments. Machine learning methods have been successfully employed in finance as one of technical approaches to financial modelling. The aim of this thesis was to develop a Bitcoin price forecasting model. We started modelling by gathering various data related to Bitcoin. We then used the gathered data with XGBoost algorithm to create a forecast model. To achieve a more in-depth understanding of our model, we used the SHAP algorithm. This allowed us to get more insight into forecasts which are otherwise usually difficult to understand. We concluded that data directly related to cryptocurrency price had the highest importance in forecasting, followed by the fear and greed index.
Keywords:crypto currency, machine learning, XGBoost, time series forecasting, SHAP


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