The paper shows how to model times series by using random iterated neural networks with place-dependent probabilities. The model assumes that the time series comes from a dynamical system which has a compact global attractor and a physical probability measure supported on the attractor. Also, an evolutionary algorithm is used to train a random iterated neural network that models a financial time series.
