Chaotic Time Series Forecasting Using Higher Order Neural Networks

Waddah Waheeb, Rozaida Ghazali


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.


Chaotic time series; Sunspot time series; Mackey-Glass time series; higher order neural network; pi-sigma neural network; functional link neural network

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Published by INSIGHT - Indonesian Society for Knowledge and Human Development