Home > Published Issues > 2023 > Volume 14, No. 4, 2023 >
JAIT 2023 Vol.14(4): 639-647
doi: 10.12720/jait.14.4.639-647

Empirical Evaluation of Machine Learning Performance in Forecasting Cryptocurrencies

Lauren Al Hawi 1, Sally Sharqawi 1, Qasem Abu Al-Haija 2,*, and Abdallah Qusef 3
1. Department of Business Intelligence Technology, Princess Sumaya University for Technology, Amman, Jordan; Email: lor20208050@std.psut.edu.jo (L.A.H.), sal20208071@std.psut.edu.jo (S.S.)
2. Department of Cybersecurity, Princess Sumaya University for Technology, Amman, Jordan
3. Department of Software Engineering, Princess Sumaya University for Technology, Amman, Jordan;
Email: a.qusef@psut.edu.jo (A.Q.)
*Correspondence: q.abualhaija@psut.eud.jo (Q.A.A.)

Manuscript received February 27, 2023; revised March 24, 2023; accepted April 7, 2023; published July 11, 2023.

Abstract—Cryptocurrencies like Bitcoin are one of today's financial system’s most contentious and difficult technological advances. This study aims to evaluate the performance of three different Machine Learning (ML) algorithms, namely, the Support Vector Machines (SVM), the K Nearest Neighbor (KNN), and the Light Gradient Boosted Machine (LGBM), which seeks to accurately estimate the price movement of Bitcoin, Ethereum, and Litecoin. To test these algorithms, we used an existing continuous dataset extracted from Kaggle and coinmarketcap.com. We implemented models using the Knime platform. We used auto biner for volume and market capital. Sensitivity analysis was performed to match different parameters. The F and accuracy statistics were used for the evaluation of algorithm performances. Empirical findings reveal that the KNN has the highest fore-casting performance for the overall dataset in our first investigation phase. On the other hand, the SVM has the highest for forecasting Bitcoin and the LGBM for Ethereum and Litecoin in the individual dataset in the second investigation phase.
Keywords—cryptocurrency, machine learning, Support Vector Machines (SVM), K Nearest Neighbor (KNN), Light Gradient Boosted Machine (LGBM), Bitcoin, Ethereum, Litecoin

Cite: Lauren Al Hawi, Sally Sharqawi, Qasem Abu Al-Haija, and Abdallah Qusef, "Empirical Evaluation of Machine Learning Performance in Forecasting Cryptocurrencies," Journal of Advances in Information Technology, Vol. 14, No. 4, pp. 639-647, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.