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Porovnanie algoritmov strojového učenia pre tvorbu predikčného modelu ceny bitcoinu
[Comparison of Machine Learning Algorithms for Creation of a Bitcoin Price Prediction Model]

Author

Listed:
  • Milan Cibuľa
  • Michal Tkáč

Abstract

With the advancement of machine learning tools, an increasing number of algorithms are being utilized for predicting not only traditional time series data related to financial markets but also those connected to cryptocurrencies. This paper aims to compare various machine learning algorithms used for prediction, in order to identify the one with the greatest practical potential for creating a prediction model of Bitcoin's price as an investment asset. The analysis focuses on supervised learning algorithms, taking into account the nature of the task involving long time series datasets. The paper also describes the exact process of creating and setting up individual models and their parameters, explaining procedures for obtaining and editing datasets, and shows how to evaluate performance of these models. In addition to the analysis of the main subject of research, which is Bitcoin, the paper also uses an analysis of reference cryptocurrencies such as Ethereum, Litecoin and NEO to compare the resulting performances. The processes consisting of editing the analysed datasets, creating individual prediction models, training and testing the performance of models on historical data, and creating, debugging and implementing individual machine learning models were realised through coding in the Python program.

Suggested Citation

  • Milan Cibuľa & Michal Tkáč, 2023. "Porovnanie algoritmov strojového učenia pre tvorbu predikčného modelu ceny bitcoinu [Comparison of Machine Learning Algorithms for Creation of a Bitcoin Price Prediction Model]," Politická ekonomie, Prague University of Economics and Business, vol. 2023(5), pages 496-517.
  • Handle: RePEc:prg:jnlpol:v:2023:y:2023:i:5:id:1397:p:496-517
    DOI: 10.18267/j.polek.1397
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    References listed on IDEAS

    as
    1. Ruoxuan Xiong & Eric P. Nichols & Yuan Shen, 2015. "Deep Learning Stock Volatility with Google Domestic Trends," Papers 1512.04916, arXiv.org, revised Feb 2016.
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    More about this item

    Keywords

    Machine learning; Bitcoin; prediction model;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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