An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products
The booming applications of bitcoin Blockchain technologies made investors concerned about the return and risk of financial products. So, the return rate of bitcoin must be foreseen in prior. This research article devises an effective return rate prediction technique for Blockchain financial product...
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doaj-6f2f48f8745c427c945fb317454481802021-03-30T02:46:37ZengIEEEIEEE Access2169-35362020-01-01812032112033010.1109/ACCESS.2020.30058089127981An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial ProductsM. Sivaram0E. Laxmi Lydia1Irina V. Pustokhina2https://orcid.org/0000-0001-5480-8871Denis Alexandrovich Pustokhin3https://orcid.org/0000-0002-8138-8494Mohamed Elhoseny4https://orcid.org/0000-0001-6347-8368Gyanendra Prasad Joshi5K. Shankar6https://orcid.org/0000-0002-2803-3846Department of Computer Networking, Lebanese French University, Erbil, IraqComputer Science and Engineering, Vignan’s Institute of Information Technology (Autonomous), Visakhapatnam, IndiaEntrepreneurship and Logistics Department, Plekhanov Russian University of Economics, Moscow, RussiaDepartment of Logistics, State University of Management, Moscow, RussiaFaculty of Computers and Information, Mansoura University, Mansoura, EgyptDepartment of Computer Science and Engineering, Sejong University, Seoul, South KoreaDepartment of Computer Applications, Alagappa University, Karaikudi, IndiaThe booming applications of bitcoin Blockchain technologies made investors concerned about the return and risk of financial products. So, the return rate of bitcoin must be foreseen in prior. This research article devises an effective return rate prediction technique for Blockchain financial products based on Optimal Least Square Support Vector Machine (OLS-SVM) model. The parameter optimization of the LS-SVM model was performed using hybridization of Grey Wolf Optimization (GWO) with Differential Evolution (DE), called optimal GWO (OGWO) algorithm. The hybridization process is performed to eliminate the local optima problem of GWO and enhance the diversity of the population. To verify the goodness of the proposed model, the Ethereum (ETH) return rate was chosen as the target and experimental analysis was performed on it to verify the predictive results on the time series. The experimental outcome was analyzed in terms of two performance measures namely Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The obtained simulation outcome infers that the OLS-SVM model yielded better predictive outcome of the return rate of financial products.https://ieeexplore.ieee.org/document/9127981/Blockchainbitcoinfinancial productsreturn rateprediction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
M. Sivaram E. Laxmi Lydia Irina V. Pustokhina Denis Alexandrovich Pustokhin Mohamed Elhoseny Gyanendra Prasad Joshi K. Shankar |
spellingShingle |
M. Sivaram E. Laxmi Lydia Irina V. Pustokhina Denis Alexandrovich Pustokhin Mohamed Elhoseny Gyanendra Prasad Joshi K. Shankar An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products IEEE Access Blockchain bitcoin financial products return rate prediction |
author_facet |
M. Sivaram E. Laxmi Lydia Irina V. Pustokhina Denis Alexandrovich Pustokhin Mohamed Elhoseny Gyanendra Prasad Joshi K. Shankar |
author_sort |
M. Sivaram |
title |
An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products |
title_short |
An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products |
title_full |
An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products |
title_fullStr |
An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products |
title_full_unstemmed |
An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products |
title_sort |
optimal least square support vector machine based earnings prediction of blockchain financial products |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
The booming applications of bitcoin Blockchain technologies made investors concerned about the return and risk of financial products. So, the return rate of bitcoin must be foreseen in prior. This research article devises an effective return rate prediction technique for Blockchain financial products based on Optimal Least Square Support Vector Machine (OLS-SVM) model. The parameter optimization of the LS-SVM model was performed using hybridization of Grey Wolf Optimization (GWO) with Differential Evolution (DE), called optimal GWO (OGWO) algorithm. The hybridization process is performed to eliminate the local optima problem of GWO and enhance the diversity of the population. To verify the goodness of the proposed model, the Ethereum (ETH) return rate was chosen as the target and experimental analysis was performed on it to verify the predictive results on the time series. The experimental outcome was analyzed in terms of two performance measures namely Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The obtained simulation outcome infers that the OLS-SVM model yielded better predictive outcome of the return rate of financial products. |
topic |
Blockchain bitcoin financial products return rate prediction |
url |
https://ieeexplore.ieee.org/document/9127981/ |
work_keys_str_mv |
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