Learning and predicting time series by neural networks
Ansgar Freking, Wolfgang Kinzel, Ido Kanter · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · 2002
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.