Abstract:
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 for his personal and professional support. I would like to sincerely thank all my teachers through the years, especially Professor Samir Das who always had time for a short question. A special acknowledgment is devoted to the Friedrich Naumann Foundation (Germany) who supported my studies in the United States. My studies would have never been possible without their support. The most important acknowledgment belongs to my parents. Their lifelong support and confidence have always strengthened me through many tough times, God bless them. ROY SCHWAERZEL
Citations
|
1092
|
Finding Structure in Time
– Elman
- 1990
|
|
696
|
Time Series Analysis: Forecasting and Control
– Box, Jenkins, et al.
- 1994
|
|
431
|
Learning representation by back propagating errors
– Rumelhart, Hinton, et al.
- 1986
|
|
366
|
Beyond Regression: New Tools for Prediction and Analysis
– Werbos
- 1974
|
|
322
|
A learning algorithm for continually running fully recurrent neural networks
– Williams, Zipser
- 1990
|
|
243
|
When networks disagree: Ensemble methods for neural network Neural networks for speech and image processing
– Perrone, Cooper
- 1993
|
|
210
|
Regularization algorithms for learning that are equivalent to multilayer networks
– Poggio, Girosi
- 1990
|
|
169
|
Gershenfeld (Eds.), Time series prediction: forecasting the future and understanding the past, NATO Advanced Research Workshop on Comparative Time series Analysis XV
– Weigand, A
- 1992
|
|
155
|
Predicting the future: a connectionist approach
– Weigand, Huberman, et al.
- 1990
|
|
145
|
The Effective Number of Parameters: An Analysis of Generalization and Regularization in Nonlinear Learning Systems
– Moody
- 1992
|
|
114
|
Modelling Financial Time Series
– TAYLOR
- 1986
|
|
93
|
The Analysis of Time Series An Introduction
– Chatfield
- 2004
|
|
80
|
Improving regression estimation: Averaging methods for variance reduction with extensions to general convex measure optimization
– Perrone
- 1993
|
|
55
|
Learning logic
– Parker
- 1985
|
|
18
|
Forecasting with Univariate Box-Jenkins Models: Concepts and Cases
– Pankratz
- 1983
|
|
17
|
Forecasting and time series analysis
– Montgomery, Johnson, et al.
- 1990
|
|
17
|
Generalization by weight-elimination applied to currency exchange rate prediction
– Weigend
- 1991
|
|
9
|
Chaos and Order in the Capital markets: A New View
– Peters
- 1991
|
|
8
|
Results of the Time Series Prediction Competition at the Santa Fe Institute
– Weigend, Gershenfeld
- 1993
|
|
7
|
Constructive learning and its application to currency exchange rate forecasting
– Refenes
- 1991
|
|
5
|
Financial prediction, some pointers, pitfalls, and common errors
– Swingler
- 1994
|
|
4
|
Time-series analysis: A comprehensive introduction for social scientists
– Gottman
- 1981
|
|
4
|
Currency Exchange Rate Forecasting by Error Backpropagation
– Refenes, Azema-Barac, et al.
- 1992
|
|
3
|
Training Feed-forward Neural Networks using Conjugate Gradients
– Blue, Grother
- 1992
|
|
3
|
Kazuo Asakawa, Morio Yoda, Masakazu Takeoka.Stock market prediction system with modular neural networks
– Kimoto
- 1990
|
|
2
|
Manual of Neural Network Simulator: FastBep Version Beta 2.0
– Rosen
- 1996
|
|
1
|
Digital Foundations of Time Series Analysis: The Box-Jenkins Approach. Holden-Day
– Robinson, Silva
- 1979
|
|
1
|
Prediction of Currency Exchange Rates by Using a Multi-Neural Neural Network System
– Schwarzel
- 1995
|
|
1
|
Japanese Influence Crows
– Sesit
- 1989
|
|
1
|
Multinational Financial Management. Allyn and
– Shapiro
- 1992
|