Time series prediction using machine learning: a case of Bitcoin returns
Studies in Economics and Finance
ISSN: 1086-7376
Article publication date: 3 November 2021
Issue publication date: 22 April 2022
Abstract
Purpose
The purpose of this study is to compare five data-driven-based ML techniques to predict the time series data of Bitcoin returns, namely, alternating model tree, random forest (RF), multiple linear regression, multi-layer perceptron regression and M5 Tree algorithms.
Design/methodology/approach
The data used to forecast time series data of Bitcoin returns ranges from 8 July 2010 to 30 Aug 2020. This study used several predictors to predict bitcoin returns including economic policy uncertainty, equity market volatility index, S&P returns, USD/EURO exchange rates, oil and gold prices, volatilities and returns. Five statistical indexes, namely, correlation coefficient, mean absolute error, root mean square error, relative absolute error and root relative squared error are determined. The results of these metrices are used to develop colour intensity ranking.
Findings
Among the machine learning (ML) techniques used in this study, RF models has shown superior predictive ability for estimating the Bitcoin returns.
Originality/value
This study is first of its kind to use and compare ML models in the prediction of Bitcoins. More studies can be carried out by using further cryptocurrencies and other ML data-driven models in future.
Keywords
Acknowledgements
Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.The author would like to specially thank Dr MNA Raja for providing guidance on methods and techniques of machine learning used in this paper.
Citation
Shakri, I.H. (2022), "Time series prediction using machine learning: a case of Bitcoin returns", Studies in Economics and Finance, Vol. 39 No. 3, pp. 458-470. https://doi.org/10.1108/SEF-06-2021-0217
Publisher
:Emerald Publishing Limited
Copyright © 2021, Emerald Publishing Limited