Proučevanje zunanjih dejavnikov pri napovedovanju cene kriptovalut s strojnim učenjem

diplomsko delo

Jakob Cvetko (Author), Sašo Karakatič (Mentor)

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;napovedovanje časovnih vrst;diplomske naloge;

Data

Language:	Slovenian
Year of publishing:	2023
Typology:	2.11 - Undergraduate Thesis
Organization:	UM FERI - Faculty of Electrical Engineering and Computer Science
Publisher:	[J. Cvetko]
UDC:	004.85:[004.7:336.74](043.2)
COBISS:	155101699
Views:	305
Downloads:	31
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary title:	External factors in predicting cryptocurrency price with machine learning
Secondary 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.
Secondary keywords:	crypto currency;machine learning;XGBoost;time series forecasting;SHAP;
Type (COBISS):	Bachelor thesis/paper
Thesis comment:	Univ. v Mariboru, Fak. za elektrotehniko, računalništvo in informatiko, Elektrotehnika
Pages:	1 spletni vir (1 datoteka PDF (VII, 33 f.))
ID:	18391051