Forecasting Market Prices with Causal-Retro-Causal Neural Networks

Forecasting of market prices is a basis of rational decision making [Zim94]. Especially recurrent neural networks (RNN) offer a framework for the computation of a complete temporal development. Our applications include short- (20 days) and long-term (52 weeks) forecast models. We describe neural networks (NN) along a correspondence principle, representing them in form of equations, architectures and embedded local algorithms.