Comparison of Machine Learning Algorithms for Creation of a Bitcoin Price Prediction Model

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.

Keywords: Machine learning, Bitcoin, prediction model