Forecasting Market Prices with Causal-Retro-Causal Neural Networks

Hans-Georg Zimmermann (), Ralph Grothmann and Christoph Tietz
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Hans-Georg Zimmermann: Siemens AG, Corporate Technology
Ralph Grothmann: Siemens AG, Corporate Technology
Christoph Tietz: Siemens AG, Corporate Technology

A chapter in Operations Research Proceedings 2011, 2012, pp 579-584 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-642-29210-1_92

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DOI: 10.1007/978-3-642-29210-1_92

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