  Time series forecasting gets much attention due to its impact on many
practical applications. Higher-order neural network with recurrent feedback is
a powerful technique which used successfully for forecasting. It maintains fast
learning and the ability to learn the dynamics of the series over time. For
that, in this paper, we propose a novel model which is called Ridge Polynomial
Neural Network with Error-Output Feedbacks (RPNN-EOFs) that combines the
properties of higher order and error-output feedbacks. The well-known
Mackey-Glass time series is used to test the forecasting capability of
RPNN-EOFS. Simulation results showed that the proposed RPNN-EOFs provides
better understanding for the Mackey-Glass time series with root mean square
error equal to 0.00416. This result is smaller than other models in the
literature. Therefore, we can conclude that the RPNN-EOFs can be applied
successfully for time series forecasting.
