Self-organization and artificial neural networks for knowledge extraction

Neural networks are widely used for nancial time series prediction. However, the future values' prediction has its drawbacks and often cannot be converted to the e ffective and pro table trading system. In that thesis I will describe several di erent types of neural networks. Then, I will propose and evaluate on real series data two di erent approaches based on Kohonen's self-organizing maps and back propagation networks of how to use those networks for creating successful and pro table trading models. Also, I will give a general overview of the Forex market (Foreign exchange market) and neural networks' usage within that market