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Forecasting Market Prices with Causal-Retro-Causal Neural Networks

In: Operations Research Proceedings 2011

Author

Listed:
  • Hans-Georg Zimmermann

    (Siemens AG, Corporate Technology)

  • Ralph Grothmann

    (Siemens AG, Corporate Technology)

  • Christoph Tietz

    (Siemens AG, Corporate Technology)

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.

Suggested Citation

  • Hans-Georg Zimmermann & Ralph Grothmann & Christoph Tietz, 2012. "Forecasting Market Prices with Causal-Retro-Causal Neural Networks," Operations Research Proceedings, in: Diethard Klatte & Hans-Jakob Lüthi & Karl Schmedders (ed.), Operations Research Proceedings 2011, edition 127, pages 579-584, Springer.
    • Handle: RePEc:spr:oprchp:978-3-642-29210-1_92
    DOI: 10.1007/978-3-642-29210-1_92
    as

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