Return Rate Prediction in Blockchain Financial Products Using Deep Learning

Recently, bitcoin-based blockchain technologies have received significant interest among investors. They have concentrated on the prediction of return and risk rates of the financial product. So, an automated tool to predict the return rate of bitcoin is needed for financial products. The recently d...

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Autores principales:	Noura Metawa, Mohamemd I. Alghamdi, Ibrahim M. El-Hasnony, Mohamed Elhoseny
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	blockchain financial products predictive model deep learning Adam optimizer LSTM model Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/a95a273f89b64a72b28d1418838d9cee
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spelling	oai:doaj.org-article:a95a273f89b64a72b28d1418838d9cee2021-11-11T19:35:40ZReturn Rate Prediction in Blockchain Financial Products Using Deep Learning10.3390/su1321119012071-1050https://doaj.org/article/a95a273f89b64a72b28d1418838d9cee2021-10-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/11901https://doaj.org/toc/2071-1050Recently, bitcoin-based blockchain technologies have received significant interest among investors. They have concentrated on the prediction of return and risk rates of the financial product. So, an automated tool to predict the return rate of bitcoin is needed for financial products. The recently designed machine learning and deep learning models pave the way for the return rate prediction process. In this aspect, this study develops an intelligent return rate predictive approach using deep learning for blockchain financial products (RRP-DLBFP). The proposed RRP-DLBFP technique involves designing a long short-term memory (LSTM) model for the predictive analysis of return rate. In addition, Adam optimizer is applied to optimally adjust the LSTM model’s hyperparameters, consequently increasing the predictive performance. The learning rate of the LSTM model is adjusted using the oppositional glowworm swarm optimization (OGSO) algorithm. The design of the OGSO algorithm to optimize the LSTM hyperparameters for bitcoin return rate prediction shows the novelty of the work. To ensure the supreme performance of the RRP-DLBFP technique, the Ethereum (ETH) return rate is chosen as the target, and the simulation results are investigated in different measures. The simulation outcomes highlighted the supremacy of the RRP-DLBFP technique over the current state of art techniques in terms of diverse evaluation parameters. For the MSE, the proposed RRP-DLBFP has 0.0435 and 0.0655 compared to an average of 0.6139 and 0.723 for compared methods in training and testing, respectively.Noura MetawaMohamemd I. AlghamdiIbrahim M. El-HasnonyMohamed ElhosenyMDPI AGarticleblockchainfinancial productspredictive modeldeep learningAdam optimizerLSTM modelEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 11901, p 11901 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	blockchain financial products predictive model deep learning Adam optimizer LSTM model Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	blockchain financial products predictive model deep learning Adam optimizer LSTM model Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Noura Metawa Mohamemd I. Alghamdi Ibrahim M. El-Hasnony Mohamed Elhoseny Return Rate Prediction in Blockchain Financial Products Using Deep Learning
description	Recently, bitcoin-based blockchain technologies have received significant interest among investors. They have concentrated on the prediction of return and risk rates of the financial product. So, an automated tool to predict the return rate of bitcoin is needed for financial products. The recently designed machine learning and deep learning models pave the way for the return rate prediction process. In this aspect, this study develops an intelligent return rate predictive approach using deep learning for blockchain financial products (RRP-DLBFP). The proposed RRP-DLBFP technique involves designing a long short-term memory (LSTM) model for the predictive analysis of return rate. In addition, Adam optimizer is applied to optimally adjust the LSTM model’s hyperparameters, consequently increasing the predictive performance. The learning rate of the LSTM model is adjusted using the oppositional glowworm swarm optimization (OGSO) algorithm. The design of the OGSO algorithm to optimize the LSTM hyperparameters for bitcoin return rate prediction shows the novelty of the work. To ensure the supreme performance of the RRP-DLBFP technique, the Ethereum (ETH) return rate is chosen as the target, and the simulation results are investigated in different measures. The simulation outcomes highlighted the supremacy of the RRP-DLBFP technique over the current state of art techniques in terms of diverse evaluation parameters. For the MSE, the proposed RRP-DLBFP has 0.0435 and 0.0655 compared to an average of 0.6139 and 0.723 for compared methods in training and testing, respectively.
format	article
author	Noura Metawa Mohamemd I. Alghamdi Ibrahim M. El-Hasnony Mohamed Elhoseny
author_facet	Noura Metawa Mohamemd I. Alghamdi Ibrahim M. El-Hasnony Mohamed Elhoseny
author_sort	Noura Metawa
title	Return Rate Prediction in Blockchain Financial Products Using Deep Learning
title_short	Return Rate Prediction in Blockchain Financial Products Using Deep Learning
title_full	Return Rate Prediction in Blockchain Financial Products Using Deep Learning
title_fullStr	Return Rate Prediction in Blockchain Financial Products Using Deep Learning
title_full_unstemmed	Return Rate Prediction in Blockchain Financial Products Using Deep Learning
title_sort	return rate prediction in blockchain financial products using deep learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a95a273f89b64a72b28d1418838d9cee
work_keys_str_mv	AT nourametawa returnratepredictioninblockchainfinancialproductsusingdeeplearning AT mohamemdialghamdi returnratepredictioninblockchainfinancialproductsusingdeeplearning AT ibrahimmelhasnony returnratepredictioninblockchainfinancialproductsusingdeeplearning AT mohamedelhoseny returnratepredictioninblockchainfinancialproductsusingdeeplearning
_version_	1718431486033526784

Return Rate Prediction in Blockchain Financial Products Using Deep Learning

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