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Time series forecasting gets much attention due to its impact on manypractical applications. Higher-order neural network with recurrent feedback isa powerful technique which used successfully for forecasting. It maintains fastlearning and the ability to learn the dynamics of the series over time. Forthat, in this paper, we propose a novel model which is called Ridge PolynomialNeural Network with Error-Output Feedbacks (RPNN-EOFs) that combines theproperties of higher order and error-output feedbacks. The well-knownMackey-Glass time series is used to test the forecasting capability ofRPNN-EOFS. Simulation results showed that the proposed RPNN-EOFs providesbetter understanding for the Mackey-Glass time series with root mean squareerror equal to 0.00416. This result is smaller than other models in theliterature. Therefore, we can conclude that the RPNN-EOFs can be appliedsuccessfully for time series forecasting.

Multi-step Time Series Forecasting Using Ridge Polynomial Neural Network with Error-Output Feedbacks