Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
C4.5: programs for machine learning
C4.5: programs for machine learning
Exploring the Power of Genetic Search in Learning Symbolic Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Growing artificial societies: social science from the bottom up
Growing artificial societies: social science from the bottom up
Financial Prediction Using Neural Networks
Financial Prediction Using Neural Networks
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Journal of Global Optimization
An Adaptive Agent Based Economic Model
Learning Classifier Systems, From Foundations to Applications
Technical analysis: the complete resource for financial market technicians
Technical analysis: the complete resource for financial market technicians
PIRR: a methodology for distributed network management in mobile networks
WSEAS Transactions on Information Science and Applications
Analyzing the influence of overconfident investors on financial markets through agent-based model
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
ICOSSSE '09 Proceedings of the 8th WSEAS international conference on System science and simulation in engineering
Computational learning techniques for intraday FX trading using popular technical indicators
IEEE Transactions on Neural Networks
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In this work, we discuss a computational technique to model financial time series combining a learning component with a simulation one. An agent based model of the financial market is used to simulate how the market will evolve in the short term while the learning component based on evolutionary computation is used to optimize the simulation parameters. Our experimentations on the DJIA and SP500 time series show the effectiveness of our learning simulation system in their modeling. Also we test its robustness under several experimental conditions and we compare the predictions made by our system to those obtained by other approaches. Our results show that our system is as good as, if not better than, alternative approaches to modeling financial time series. Moreover we show that our approach requires a simple input, the time series for which a model has to be learned, versus the complex and feature rich input to be given to other systems thanks to the ability of our system to adjust its parameters by learning.