SHS Web Conf.
Volume 65, 2019The 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2019)
|Number of page(s)||7|
|Section||Machine Learning for Prediction of Emergent Economy Dynamics|
|Published online||29 May 2019|
Forecasting cryptocurrency prices time series using machine learning approach
Kyiv National Economic University named after Vadym Hetman, Department of Informatics and Systemology, Kyiv, Ukraine
2 Kyiv National Economic University named after Vadym Hetman, Economic and Mathematical Modelling Department, Kyiv, Ukraine
3 Kyiv National Economic University named after Vadym Hetman, Economics Information Systems Department, Kyiv, 03057, Ukraine
* Corresponding author: firstname.lastname@example.org
This paper describes the construction of the short-term forecasting model of cryptocurrencies’ prices using machine learning approach. The modified model of Binary Auto Regressive Tree (BART) is adapted from the standard models of regression trees and the data of the time series. BART combines the classic algorithm classification and regression trees (C&RT) and autoregressive models ARIMA. Using the BART model, we made a short-term forecast (from 5 to 30 days) for the 3 most capitalized cryptocurrencies: Bitcoin, Ethereum and Ripple. We found that the proposed approach was more accurate than the ARIMA-ARFIMA models in forecasting cryptocurrencies time series both in the periods of slow rising (falling) and in the periods of transition dynamics (change of trend).
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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