Comparative performance of machine learning algorithms for cryptocurrency forecasting

Hitam, Nor Azizah and Ismail, Amelia Ritahani (2018) Comparative performance of machine learning algorithms for cryptocurrency forecasting. Indonesian Journal of Electrical Engineering and Computer Science, 11 (3). 1121 -1128. ISSN 2502-4752

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Abstract

Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a result that is almost or close to actual result yet also improve the accuracy of the result itself. However, recent research has showed that due to small range of samples and data manipulation by inadequate evidence and professional analyzers, overall status and accuracy rate of the forecasting needs to be improved in further studies. Thus, advanced research on the accuracy rate of the forecasted price has to be done.

Item Type:	Article (Journal)
Additional Information:	4296/65781
Uncontrolled Keywords:	Artificial Intelligence, Machine Learning, Support Vector , Machines, Neural Networks, Deep Learning,
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Amelia Ritahani Ismail
Date Deposited:	04 Sep 2018 16:16
Last Modified:	02 Jan 2024 10:23
URI:	http://irep.iium.edu.my/id/eprint/65781

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