Roy Schwaerzel

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This paper describes an extension of the traditional application of Genetic Programming in the domain of the prediction of daily currency exchange rates. In combination with trigonometric operators, we introduce a new set of high-order statistical functions in a unique representation and analyze each system performance using daily returns of the British(More)
Acknowledgements My thanks go to my adviser, Professor Bruce E. Rosen, for his guidance and support throughout the work of this thesis. His insights and discussions has been very valuable for this thesis and for myself. I am grateful to Professor Robert Hiromoto, for many helpful discussions that enhanced the application perspective of this thesis. Thanks(More)
We apply neural network ensembles to the task of forecasting financial time series and explore the use of high order statistical information as part of network inputs. We show that the prediction accuracy of the time series can be significanlty improved utilizing this methodology. Since prediction accuracy is only an estimate for the profitability on the(More)
This paper describes an extension of the traditional application of Genetic Programming in the domain of the prediction of daily currency exchange rates. In combination with trigonometric operators, we introduce a new set of high-order statistical functions in a unique representation and analyze their performance using daily returns of the British Pound and(More)
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