Evaluating interpretable machine learning predictions for cryptocurrencies
Journal article
Majzoub, Ahmad El, Rabhi, Fethi A. and Hussain, Walayat. (2023). Evaluating interpretable machine learning predictions for cryptocurrencies. Intelligent Systems in Accounting, Finance and Management. pp. 1-13. https://doi.org/10.1002/isaf.1538
Authors | Majzoub, Ahmad El, Rabhi, Fethi A. and Hussain, Walayat |
---|---|
Abstract | This study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N-BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources. |
Keywords | artificial intelligence; cryptocurrency; deep learning; interpretability; machine learning; technical indicators; time series forecasting |
Year | 2023 |
Journal | Intelligent Systems in Accounting, Finance and Management |
Journal citation | pp. 1-13 |
Publisher | John Wiley & Sons Ltd |
ISSN | 2160-0074 |
Digital Object Identifier (DOI) | https://doi.org/10.1002/isaf.1538 |
Scopus EID | 2-s2.0-85162644788 |
Open access | Published as ‘gold’ (paid) open access |
Page range | 1-13 |
Publisher's version | License File Access Level Open |
Output status | Published |
Publication dates | |
Online | 21 Jun 2023 |
Publication process dates | |
Accepted | 29 May 2023 |
Deposited | 08 Aug 2023 |
https://acuresearchbank.acu.edu.au/item/8z79x/evaluating-interpretable-machine-learning-predictions-for-cryptocurrencies
Download files
Publisher's version
OA_Majzoub_2023_Evaluating_interpretable_machine_learning_predictions_for.pdf | |
License: CC BY-NC-ND 4.0 | |
File access level: Open |
71
total views44
total downloads0
views this month0
downloads this month