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Forecasting Market Prices with Causal-Retro-Causal Neural Networks

  • Hans-Georg Zimmermann
  • Ralph Grothmann
  • Christoph Tietz
Conference paper
Part of the Operations Research Proceedings book series (ORP)

Abstract

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.

Keywords

State Space Model Recurrent Neural Network Forecast Accuracy Forecast Horizon London Metal Exchange 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hans-Georg Zimmermann
    • 1
  • Ralph Grothmann
    • 1
  • Christoph Tietz
    • 1
  1. 1.Siemens AG, Corporate TechnologyMunichGermany

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