Time series prediction using chaotic neural networks: case study of IJCNN CATS benchmark test

KIII is a strongly biologically inspired neural network model. It has a multi-layer architecture with excitatory and inhibitory neurons, which have massive lateral, feedforward, and delayed feedback connections between layers. KIII has been shown previously to be an efficient tool of classification and pattern recognition. In this work, we develop a methodology to use KIII for multi-step time series prediction. The method is applied for the IJCNN CATS benchmark data.