Learning and predicting time series by neural networks.
Artificial neural networks which are trained on a time series are supposed to achieve two abilities: first, to predict the series many time steps ahead and second, to learn the rule which has produced the series. It is shown that prediction and learning are not necessarily related to each other. Chaotic sequences can be learned but not predicted while quasiperiodic sequences can be well predicted but not learned.
Freking
Ansgar
A
Institut für Theoretische Physik und Astrophysik, Universität Würzburg, Am Hubland, 97074 Würzburg, Germany.
Kinzel
Wolfgang
W
Kanter
Ido
I
D000465:Algorithms; D001185:Artificial Intelligence; D007858:Learning; D015233:Models, Statistical; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D015203:Reproducibility of Results; D013997:Time Factors