In this paper the Nikkei stock prices over 1500 days, from July 1996 to Oct. 2002, are analyzed and predicted using a Hurst exponent (H), a fractal dimension (D), and an autocorrelation coefficient (C). They are
and
over three days. In order to extract the knowledge, decision making rules comprehensible by humans using the features are derived by rough set theory. Then, this obtained knowledge is embedded into the structure of our developed time delayed neural network (Shafique and Dote 2000). It is a back-propagation neural network with a FIR (Finite Impulse Response) filter of the second order plugged into each time delayed input node. It is confirmed that the obtained prediction accuracy is much higher than that obtained by a back propagation-type forward neural network without filters for the short-term. Therefore, this predictor is one of hybrid intelligent systems, which is expected to be a promising approach in the future.
