Abstract
Predicting time series is a quite important field of economic research. Neural networks provide a quite good access for analyzing nonlinear time series. Furthermore, they provide a tool for accessing problems with an unknown structure. Nevertheless it would be sometimes useful to have an access where one can put existing knowledge into the models to improve their prediction quality, and to have a more interpreterable, i.e. reliable model. Fuzzy neural networks provide such an access.
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References
Ledermann J., Klein R. A., eds. Virtual Trading. Chicago: Probus Publishing, 1995.
London Business School. Proceedings of the Neural Networks in the Capital Markets, 1994.
M. Rast. Application of Fuzzy Neural Networks on Financial Problems. In Proceedings of the NAFIPS’97, Işik C., Cross V., eds., IEEE, 1997.
M. Rast. Forecasting Financial Time Series with Fuzzy Neural Networks. In Proceedings of the 1997 IEEE International Conference on Intelligent Processing Systems, IEEE, 1997.
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© 1998 Springer Science+Business Media Dordrecht
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Rast, M. (1998). Predicting Time Series with a Committee of Independent Experts Based on Fuzzy Rules. In: Refenes, AP.N., Burgess, A.N., Moody, J.E. (eds) Decision Technologies for Computational Finance. Advances in Computational Management Science, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5625-1_35
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DOI: https://doi.org/10.1007/978-1-4615-5625-1_35
Publisher Name: Springer, Boston, MA
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