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TechRxiv

Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting Method

作     者:Krichene, Emna Ouarda, Wael Chabchoub, Habib Abraham, Ajith Qahtani, Abdulrahman M. Almutiry, Omar Dhahri, Habib Alimi, Adel M. 

作者机构: BP 1173 Sfax3038 Tunisia Digital Research Center of Sfax B.P. 275 Sakiet Ezzit Sfax3021 Tunisia College of Business Al Ain University of Science and Technology United Arab Emirates  Scientific Network for Innovation and Research Excellence WA98071-2259 United States Department of Computer Science College of Computers and Information Technology Taif University P.O.Box. 11099 Taif21944 Saudi Arabia College of Applied Computer Science King Saud University Riyadh Saudi Arabia Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa 

出 版 物:《TechRxiv》 (TechRxiv)

年 卷 期:2021年

核心收录:

主  题:Recurrent neural networks 

摘      要:A newly introduced method called Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting method is applied and extended in this study to forecast numerical values. Unlike traditional forecasting techniques which forecast only future values, our proposed method provides a new extension to correct the predicted values which is done by forecasting the estimated error. Experimental results demonstrated that the proposed method has a high accuracy both in training and testing data and outperform the state-of-the-art RNN models on Mackey-Glass, NARMA, Lorenz and Henon map datasets. © 2021, CC BY.

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