TechRxiv
Applied_Intelligence-f.pdf (806.39 kB)
Download file

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

Download (806.39 kB)
preprint
posted on 28.12.2021, 19:49 by Emna KricheneEmna Krichene, Wael OuardaWael Ouarda, Habib Chabchoub, Ajith Abraham, Abdulrahman M. Qahtani, Omar Almutiry, habib dhahri, Adel AlimiAdel Alimi
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.

History

Email Address of Submitting Author

emna.krichen@enis.tn

Submitting Author's Institution

National School of Engineering of Sfax

Submitting Author's Country

Tunisia

Usage metrics

Licence

Exports