Taylor-based Optimized Recursive Extended Exponential Smoothed Neural
Networks Forecasting Method
- Emna Krichene ,
- Wael Ouarda ,
- Habib Chabchoub ,
- Ajith Abraham ,
- Abdulrahman M. Qahtani ,
- Omar Almutiry ,
- habib dhahri ,
- Adel Alimi
Abstract
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.