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

Authors :
Krichene, Emna
Ouarda, Wael
Chabchoub, Habib
Alimi, Adel M.
Publication Year :
2018

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.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.1811.00323
Document Type :
Working Paper