Forecasting cryptocurrency prices time series using machine learning approach

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 c...

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Bibliographic Details
Published inSHS Web of Conferences Vol. 65; p. 2001
Main Authors Derbentsev, Vasily, Datsenko, Natalia, Stepanenko, Olga, Bezkorovainyi, Vitaly
Format Journal Article Conference Proceeding
LanguageEnglish
Published Les Ulis EDP Sciences 2019
Subjects
Artificial intelligence
Digital currencies
Machine learning
Regression analysis
Time series
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Summary: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).
ISSN:2261-2424
2416-5182
2261-2424
DOI:10.1051/shsconf/20196502001