Prediction with recurrent networks
N. H. Wulff, John Hertz · 2003
The authors study extrapolation of time series using recurrent neural networks. They use the real-time recurrent learning algorithm introduced by R. J. Williams and D. Zipser (1989), both in the original form for first order nets and in a form for second order nets. It is shown that both the first order and the second order nets are able to learn to simulate the Mackey-Glass series. The prediction quality of the results is comparable to that from feedforward nets.>