Using the optimization layer-by-layer learning algorithm on local-recurrent-global-feedforward networks in financial time series predictions
Jun Feng Lu, Hiroshi Ohta · International Journal of Systems Science · 2002
In the field of time series prediction, neural networks are widely used and they have been proven useful and practical. To improve the prediction ability and to reduce the time consumption of neural networks, neural networks are usually developed by researchers and practitioners from learning algorithms, network architectures, etc. A local-recurrent-global-feedforward (LRGF) network using a learning algorithm called the optimization layer-by-layer (OLL) method is proposed. In addition, two representative LRGF networks are introduced: the finite impulse response (FIR) network and the FGS network (proposed by Frasconi, Gori and Soda), and a comparative simulation predicting several financial time series using both methods is performed. According to the results of the simulation, the FGS-OLL method gives better predicting performance than the FIR-OLL method.