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arXiv · 1811.11620

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

Waddah Waheeb·Rozaida Ghazali
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Abstract

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.

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Waddah Waheeb, Rozaida Ghazali. 2018-11-28. Multi-step Time Series Forecasting Using Ridge Polynomial Neural Network with Error-Output Feedbacks. https://doi.org/10.1007/978-981-10-2777-2_5

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