Financial time series forecasting using twin support vector regression
Financial time series forecasting is a crucial measure for improving and making more robust financial decisions throughout the world. Noisy data and non-stationarity information are the two key factors in financial time series prediction. This paper proposes twin support vector regression for financ...
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sg-ntu-dr.10356-1059592019-12-06T22:01:36Z Financial time series forecasting using twin support vector regression Gupta, Deepak Pratama, Mahardhika Ma, Zhenyuan Li, Jun Prasad, Mukesh Martínez-Álvarez, Francisco School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering Finance Stock Markets Financial time series forecasting is a crucial measure for improving and making more robust financial decisions throughout the world. Noisy data and non-stationarity information are the two key factors in financial time series prediction. This paper proposes twin support vector regression for financial time series prediction to deal with noisy data and nonstationary information. Various interesting financial time series datasets across a wide range of industries, such as information technology, the stock market, the banking sector, and the oil and petroleum sector, are used for numerical experiments. Further, to test the accuracy of the prediction of the time series, the root mean squared error and the standard deviation are computed, which clearly indicate the usefulness and applicability of the proposed method. The twin support vector regression is computationally faster than other standard support vector regression on the given 44 datasets. Published version 2019-06-19T04:04:21Z 2019-12-06T22:01:36Z 2019-06-19T04:04:21Z 2019-12-06T22:01:36Z 2019 Journal Article Gupta, D., Pratama, M., Ma, Z., Li, J., & Prasad, M. (2019). Financial time series forecasting using twin support vector regression. PLOS ONE, 14(3), e0211402-. doi:10.1371/journal.pone.0211402 https://hdl.handle.net/10356/105959 http://hdl.handle.net/10220/48827 http://dx.doi.org/10.1371/journal.pone.0211402 en PLOS ONE © 2019 Guptaet al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 27 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering Finance Stock Markets Gupta, Deepak Pratama, Mahardhika Ma, Zhenyuan Li, Jun Prasad, Mukesh Financial time series forecasting using twin support vector regression |
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Financial time series forecasting is a crucial measure for improving and making more robust financial decisions throughout the world. Noisy data and non-stationarity information are the two key factors in financial time series prediction. This paper proposes twin support vector regression for financial time series prediction to deal with noisy data and nonstationary information. Various interesting financial time series datasets across a wide range of industries, such as information technology, the stock market, the banking sector, and the oil and petroleum sector, are used for numerical experiments. Further, to test the accuracy of the prediction of the time series, the root mean squared error and the standard deviation are computed, which clearly indicate the usefulness and applicability of the proposed method. The twin support vector regression is computationally faster than other standard support vector regression on the given 44 datasets. |
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Martínez-Álvarez, Francisco |
author_facet |
Martínez-Álvarez, Francisco Gupta, Deepak Pratama, Mahardhika Ma, Zhenyuan Li, Jun Prasad, Mukesh |
format |
Article |
author |
Gupta, Deepak Pratama, Mahardhika Ma, Zhenyuan Li, Jun Prasad, Mukesh |
author_sort |
Gupta, Deepak |
title |
Financial time series forecasting using twin support vector regression |
title_short |
Financial time series forecasting using twin support vector regression |
title_full |
Financial time series forecasting using twin support vector regression |
title_fullStr |
Financial time series forecasting using twin support vector regression |
title_full_unstemmed |
Financial time series forecasting using twin support vector regression |
title_sort |
financial time series forecasting using twin support vector regression |
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2019 |
url |
https://hdl.handle.net/10356/105959 http://hdl.handle.net/10220/48827 http://dx.doi.org/10.1371/journal.pone.0211402 |
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1681048185901940736 |