Learning and Predicting Financial Time Series by Combining Natural Computation and Agent Simulation

We investigate how, by combining natural computation and agent based simulation, it is possible to model financial time series. The agent based simulation can be used to functionally reproduce the structure of a financial market while the natural computation technique finds the most suitable parameter for the simulator. Our experimentation on the DJIA time series shows the effectiveness of this approach in modeling financial data. Also we compare the predictions made by our system to those obtained by other approaches.

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