Time Series Prediction Using Dynamic Ridge Polynomial Neural Networks

Novel higher order polynomial neural network architecture is presented in this paper. The new proposed neural network is called dynamic ridge polynomial neural network that combines the properties of higher order and recurrent neural networks. The advantage of this type of network is that it exploits the properties of higher-order neural networks by functionally extending the input space into a higher dimensional space, where linear separability is possible, without suffering from the combinatorial explosion in the number of weights. Furthermore, the network has a regular structure, since the order can be suitably augmented by additional sigma units. Finally, the presence of the recurrent link expands the network's ability for attractor dynamics and storing information for later use. The performance of the network is tested for the prediction of nonlinear and nonstationary time series. Two popular time series, the Lorenz attractor and the mean value of the AE index, are used in our studies. The simulation results showed better results in terms of the signal to noise ratio in comparison to a number of higher order and feedforward networks.