Comparative performance of machine learning algorithms for cryptocurrency forecasting

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. Th...

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Main Authors:	Hitam, Nor Azizah, Ismail, Amelia Ritahani
Format:	Article
Language:	English English
Published:	Institute of Advanced Engineering and Science 2018
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/65781/ http://irep.iium.edu.my/65781/ http://irep.iium.edu.my/65781/ http://irep.iium.edu.my/65781/7/65781_Comparative%20performance%20of%20machine%20learning%20algorithms_SCOPUS.pdf http://irep.iium.edu.my/65781/13/65781_Comparative%20performance%20of%20machine.pdf

id	iium-65781
recordtype	eprints
spelling	iium-657812018-09-12T04:05:32Z http://irep.iium.edu.my/65781/ Comparative performance of machine learning algorithms for cryptocurrency forecasting Hitam, Nor Azizah Ismail, Amelia Ritahani QA75 Electronic computers. Computer science 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. Institute of Advanced Engineering and Science 2018-09 Article PeerReviewed application/pdf en http://irep.iium.edu.my/65781/7/65781_Comparative%20performance%20of%20machine%20learning%20algorithms_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/65781/13/65781_Comparative%20performance%20of%20machine.pdf 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 http://iaescore.com/journals/index.php/IJEECS/article/view/13469 10.11591/ijeecs.v11.i3.pp1121-1128
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Hitam, Nor Azizah Ismail, Amelia Ritahani Comparative performance of machine learning algorithms for cryptocurrency forecasting
description	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.
format	Article
author	Hitam, Nor Azizah Ismail, Amelia Ritahani
author_facet	Hitam, Nor Azizah Ismail, Amelia Ritahani
author_sort	Hitam, Nor Azizah
title	Comparative performance of machine learning algorithms for cryptocurrency forecasting
title_short	Comparative performance of machine learning algorithms for cryptocurrency forecasting
title_full	Comparative performance of machine learning algorithms for cryptocurrency forecasting
title_fullStr	Comparative performance of machine learning algorithms for cryptocurrency forecasting
title_full_unstemmed	Comparative performance of machine learning algorithms for cryptocurrency forecasting
title_sort	comparative performance of machine learning algorithms for cryptocurrency forecasting
publisher	Institute of Advanced Engineering and Science
publishDate	2018
url	http://irep.iium.edu.my/65781/ http://irep.iium.edu.my/65781/ http://irep.iium.edu.my/65781/ http://irep.iium.edu.my/65781/7/65781_Comparative%20performance%20of%20machine%20learning%20algorithms_SCOPUS.pdf http://irep.iium.edu.my/65781/13/65781_Comparative%20performance%20of%20machine.pdf
first_indexed	2023-09-18T21:33:19Z
last_indexed	2023-09-18T21:33:19Z
_version_	1777412655745597440

Comparative performance of machine learning algorithms for cryptocurrency forecasting

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