  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.
