Embedding coupled oscillators into a feedforward architecture for improved time series prediction

The network defined by Hayashi (1994), like many purely recurrent networks, has proven very difficult to train to arbitrary time series. Many recurrent architectures are best suited for producing specific cyclic behaviors. As a result, a hybrid network has been developed to allow for training to more general sequences. The network used here is a combination of standard feedforward nodes and Hayashi oscillator pairs. A learning rule, developed using a discrete mathematics approach, is presented for the hybrid network. Significant improvements in prediction accuracy were produced compared to a pure Hayashi network and a backpropagation network. Data sets used for testing the effectiveness of this approach include Mackey-Glass, sunspot, and ECG data. The hybrid models reduced training and testing error in each case by a least 34%