  Artificial neural networks which are trained on a time series are supposed to
achieve two abilities: firstly to predict the series many time steps ahead and
secondly 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.
