Abstract: In order to improve the prediction accuracy of echo state network for time series, this paper uses a modified small world network to improve leaky-integrator echo state network (Leaky ESN) and then uses the improved Leaky ESN to predict time series. Firstly, we propose a modified small world network whose adding edge probability is a negative exponential function of the distance between the nodes. Then, we use adding edge probability to directly represent the connection weights of two neural nodes in the reservoir of Leaky ESN. The value of the connection weights belongs to [0,1] and represents the degree of connection between the two nodes. The modified small world network is utilized to improve the connection of reservoir neural nodes, which purposefully achieves the sparse connection to reduce the blindness of the original sparse connections and then improves the adaptive capacity of the reservoir. Finally, we use the modified Leaky ESN to predict a typical nonlinear time series. We use Matlab to verify the effectiveness of the proposed method. Compared with Leaky ESN, the method proposed in this paper has a higher prediction precision and a shorter training time.