STOCK PRICE PREDICTION: KOHONEN VERSUS BACKPROPAGATION
А. В. Зорин · 2003
This paper describes the application of two diffbrent neural network types for stock price prediction. The prediction is carried out by Kohonen self-organizing maps and error backpropagation algorithm. Both experimental networks deal with price change intervals in contradiction to precise value prediction. The results are presented and its comparative analysis is performed in this paper, as well as a short discussion on both neural network architectures and learning algorithms