Multi-step Time Series Forecasting Using Ridge Polynomial Neural Network with Error-Output Feedbacks
This addresses forecasting accuracy for time series applications, but appears incremental as it builds on existing higher-order neural network techniques.
The paper tackles multi-step time series forecasting by proposing a Ridge Polynomial Neural Network with Error-Output Feedbacks (RPNN-EOFs), which achieved a root mean square error of 0.00416 on the Mackey-Glass time series, outperforming other models.
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.