Adaptive lasso echo state network for time series prediction

Echo state network (ESN), a novel recurrent neural network, has a randomly and sparsely connected reservoir. Since the reservoir is very large, the collinearity problem may exist in ESN. To overcome this problem and get a sparse architecture, an adaptive lasso echo state network (ALESN) is proposed, in which the adaptive lasso algorithm is used to calculate the output weights. The proposed ALESN can deal with the collinearity problem and has the oracle property. Simulation results show that the proposed ALESN has better performance and more compact architecture than some other existing methods.