Abstract
Business management requires precise forecasts in order to enhance the quality of planning throughout the value chain. Furthermore, the uncertainty in forecasting has to be taken into account.
Keywords
- Recurrent Neural Network
- Hide Variable
- Neural Network Architecture
- Partial Observability
- Individual Forecast
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Calvert D. and Kremer St.: Networks with Adaptive State Transitions, in: Kolen J. F. and Kremer, St. (Ed.): A Field Guide to Dynamical Recurrent Networks, IEEE, 2001, pp. 15–25.
Föllmer, H.: Alles richtig und trotzdem falsch?, Anmerkungen zur Finanzkrise und Finanz-mathematik, in: MDMV 17/2009, pp. 148–154
Haykin S.: Neural Networks and Learning Machines, 3rd Edition, Prentice Hall, 2008.
Hull, J.: Options, Futures & Other Derivative Securities. Prentice Hall, 2001.
McNeil, A., Frey, R. and Embrechts, P.: Quantitative Risk Management: Concepts, Techniques and Tools, Princeton University Press, Princeton, New Jersey, 2005.
Pearlmatter B.: Gradient Calculations for Dynamic Recurrent Neural Networks, in: A Field Guide to Dynamical Recurrent Networks, Kolen, J.F.; Kremer, St. (Ed.); IEEE Press, 2001, pp. 179–206.
Schäfer, A. M. und Zimmermann, H.-G.: Recurrent Neural Networks Are Universal Approx-imators. ICANN, Vol. 1., 2006, pp. 632–640.
Wei W. S.: Time Series Analysis: Univariate and Multivariate Methods, Addison-Wesley Publishing Company, N.Y., 1990.
Werbos P. J.: Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, PhD Thesis, Harvard University, 1974.
Williams R. J. and Zipser, D.: A Learning Algorithm for continually running fully recurrent neural networks, Neural Computation, Vol. 1, No. 2, 1989, pp. 270–280.
Zimmermann, H. G., Grothmann, R. and Neuneier, R.: Modeling of Dynamical Systems by Error Correction Neural Networks. In: Soofi, A. und Cao, L. (Ed.): Modeling and Forecasting Financial Data, Techniques of Nonlinear Dynamics, Kluwer, 2002.
Zimmermann, H. G., Grothmann, R., Schäfer, A. M. and Tietz, Ch.: Modeling Large Dynamical Systems with Dynamical Consistent Neural Networks, in New Directions in Statistical Signal Processing: From systems to brain. Haykin, S., Principe, J. C., Sejnowski, T. J., and McWhirter, J. (Ed.), MIT Press, Cambridge, Mass., 2006.
Zimmermann, H. G.: Neuronale Netze als Entscheidungskalkül. In: Rehkugler, H. und Zim-mermann, H. G. (Ed.): Neuronale Netze in der ßkonomie, Grundlagen und wissenschaftliche Anwendungen, Vahlen, Munich 1994.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zimmermann, HG., Grothmann, R., Tietz, C., von Jouanne-Diedrich, H. (2011). Market Modeling, Forecasting and Risk Analysis with Historical Consistent Neural Networks. In: Hu, B., Morasch, K., Pickl, S., Siegle, M. (eds) Operations Research Proceedings 2010. Operations Research Proceedings. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20009-0_84
Download citation
DOI: https://doi.org/10.1007/978-3-642-20009-0_84
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20008-3
Online ISBN: 978-3-642-20009-0
eBook Packages: Business and EconomicsBusiness and Management (R0)