Derbentsev, VasilyДербенцев, Василь ДжоржовичDatsenko, NataliaДаценко, Наталія ВолодимирівнаStepanenko, OlgaСтепаненко, Ольга ПетрівнаBezkorovainyi, VitalyБезкоровайний, Віталій Сергійович2021-10-122021-10-122019-05Forecasting cryptocurrency prices time series using machine learning approach [Electronic resource] / Vasily Derbentsev, Natalia Datsenko, Olga Stepanenko, Vitaly Bezkorovainyi1 // SHS Web of Conferences : the 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2019), Odessa, Ukraine, May 22–24, 2019 / [ed. board: S. Semerikov et al.]. – Electronic text data. – Odessa, 2019. – Vol. 65. – Mode of access: https://doi.org/10.1051/shsconf/20196502001. – Title from screen.2261-2424https://ir.kneu.edu.ua:443/handle/2010/36616This 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).enArticle