The effect of kernel functions on cryptocurrency prediction using support vector machines
Forecasting in the financial sector has proven to be a highly important 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 functi...
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| Format: | Book Section |
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Springer Science and Business Media Deutschland GmbH
2022
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| _version_ | 1796866831742402560 |
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| author | Ismail, Amelia Ritahani Hitam, Nor Azizah Samsudin, Ruhaidah Alkhammash, Eman H. |
| author_facet | Ismail, Amelia Ritahani Hitam, Nor Azizah Samsudin, Ruhaidah Alkhammash, Eman H. |
| author_sort | Ismail, Amelia Ritahani |
| collection | ePrints |
| description | Forecasting in the financial sector has proven to be a highly important 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, sentiment 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 outcomes. 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. |
| first_indexed | 2024-03-05T21:18:05Z |
| format | Book Section |
| id | utm.eprints-100073 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T21:18:05Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | utm.eprints-1000732023-04-04T06:58:51Z http://eprints.utm.my/100073/ The effect of kernel functions on cryptocurrency prediction using support vector machines Ismail, Amelia Ritahani Hitam, Nor Azizah Samsudin, Ruhaidah Alkhammash, Eman H. QA75 Electronic computers. Computer science Forecasting in the financial sector has proven to be a highly important 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, sentiment 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 outcomes. 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. Springer Science and Business Media Deutschland GmbH 2022 Book Section PeerReviewed Ismail, Amelia Ritahani and Hitam, Nor Azizah and Samsudin, Ruhaidah and Alkhammash, Eman H. (2022) The effect of kernel functions on cryptocurrency prediction using support vector machines. In: Advances on Intelligent Informatics and Computing Health Informatics, Intelligent Systems, Data Science and Smart Computing. Lecture Notes on Data Engineering and Communications Technologies, 127 (NA). Springer Science and Business Media Deutschland GmbH, Cham, Switzerland, pp. 319-332. ISBN 978-3-030-98740-4 http://dx.doi.org/10.1007/978-3-030-98741-1_27 DOI : 10.1007/978-3-030-98741-1_27 |
| spellingShingle | QA75 Electronic computers. Computer science Ismail, Amelia Ritahani Hitam, Nor Azizah Samsudin, Ruhaidah Alkhammash, Eman H. The effect of kernel functions on cryptocurrency prediction using support vector machines |
| title | The effect of kernel functions on cryptocurrency prediction using support vector machines |
| title_full | The effect of kernel functions on cryptocurrency prediction using support vector machines |
| title_fullStr | The effect of kernel functions on cryptocurrency prediction using support vector machines |
| title_full_unstemmed | The effect of kernel functions on cryptocurrency prediction using support vector machines |
| title_short | The effect of kernel functions on cryptocurrency prediction using support vector machines |
| title_sort | effect of kernel functions on cryptocurrency prediction using support vector machines |
| topic | QA75 Electronic computers. Computer science |
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