Chaotic Time Series Forecasting Using Higher Order Neural Networks

Waddah Waheeb (1), Rozaida Ghazali (2)
(1) Universiti Tun Hussein Onn Malaysia
(2) Universiti Tun Hussein Onn Malaysia
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How to cite (IJASEIT) :
Waheeb, Waddah, and Rozaida Ghazali. “Chaotic Time Series Forecasting Using Higher Order Neural Networks”. International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 5, Oct. 2016, pp. 624-9, doi:10.18517/ijaseit.6.5.958.
This study presents a novel application and comparison of higher order neural networks (HONNs) to forecast benchmark chaotic time series. Two models of HONNs were implemented, namely functional link neural network (FLNN) and pi-sigma neural network (PSNN). These models were tested on two benchmark time series; the monthly smoothed sunspot numbers and the Mackey-Glass time-delay differential equation time series. The forecasting performance of the HONNs is compared against the performance of different models previously used in the literature such as fuzzy and neural networks models. Simulation results showed that FLNN and PSNN offer good performance compared to many previously used hybrid models.

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