Efficient automatic systems which continuously learn over long periods of time, and manage to evolve its knowledge, by discarding obsolete parts of it and acquiring new ones to reflect recent data, are difficult to be constructed. This paper addresses neural network (NN) learning in non-stationary environments related to financial markets, aiming at forecasting stock closing price. To face up this...
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.