title: Learning and predicting time series by neural networks.
abstract: 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.
authors.affiliation: Institut für Theoretische Physik und Astrophysik, Universität Würzburg, Am Hubland, 97074 Würzburg, Germany.
authors.forename: Ansgar
authors.initials: A
authors.lastname: Freking
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Kinzel
authors.forename: Ido
authors.initials: I
authors.lastname: Kanter
mesh_terms: 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