The effect of Kernel functions on cryptocurrency prediction using support vector machines

Hitam, Nor Azizah and Ismail, Amelia Ritahani and Samsuddin, Ruhaidah and Alkhammash, Eman H. (2022) The effect of Kernel functions on cryptocurrency prediction using support vector machines. In: Lecture Notes on Data Engineering and Communications Technologies (LNDECT). Lecture Notes on Data Engineering and Communications Technologie, 127 . Springer Science and Business Media Deutschland GmbH, pp. 319-332. ISBN 978-3-030-98740-4

Preview	PDF (SCOPUS) - Supplemental Material Download (547kB) | Preview
	PDF - Published Version Restricted to Registered users only Download (721kB) | Request a copy

Abstract

Forecastinginthefinancialsectorhasproventobeahighlyimportant area of study in the science of Computational Intelligence (CI). Furthermore, the availability of social media platforms contributes to the advancement of SVM research and the selection of SVM parameters. Using SVM kernel functions, this study examines the four kernel functions available: Linear, Radial Basis Gaussian (RBF), Polynomial, and Sigmoid kernels, for the purpose of cryptocurrency and foreign exchange market prediction. The available technical numerical data, senti- ment data, and a technical indicator were used in this experimental research, which was conducted in a controlled environment. The cost and epsilon-SVM regression techniques are both being utilised, and they are both being performed across the five datasets in this study. On the basis of three performance measures, which are the MAE, MSE, and RMSE, the results have been compared and assessed. The forecasting models developed in this research are used to predict all of the out- comes. The SVM-RBF kernel forecasting model, which has outperformed other SVM-kernel models in terms of error rate generated, are presented as a conclusion to this study.

Item Type:	Book Chapter
Uncontrolled Keywords:	Cryptocurrency · Computational Intelligence (CI) · Support Vector Machine (SVM) · Radial Basis Gaussian (RBF) kernel · Linear Kernel · Polynomial Kernel · Sigmoid Kernels
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:	24 Jul 2022 21:36
Last Modified:	25 Jul 2022 08:45
URI:	http://irep.iium.edu.my/id/eprint/98900

Actions (login required)

View Item