Efficient hybrid neural network for chaotic time series prediction

We propose an efficient hybrid neural network for chaotic time series prediction. The hybrid neural network is constructed by a traditional feed-forward network, which is learned by using the backpropagation and a local model, which is implemented as a time delay embedding. The feed-forward network performs as the global approximation and the local model works as the local approximation. Experimental results using Mackey-Glass data and K.U. Leuven competition data show that the proposed method can predict the more long term than each of predictors.