The contribution of this work is twofold:(1) a multimodality prediction method of chaotic time series with the Gaussian process mixture (GPM) model is proposed,which employs a divide and conquer strategy.It auto-matically divides the chaotic time series into multiple modaJities with different extrinsic pattes and intrinsic characteristics,and thus can more precisely fit the chaotic time series.(2) An effective sparse hard-cut expectation maximization (SHC-EM) leaing algorithm for the GPM model is proposed to improve the prediction performance.SHC-EM replaces a large leaing sample set with fewer pseudo inputs,accelerating model leaing based on these pseudo inputs.Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction,but also the prediction confidence interval.SHC-EM outperforms the traditional variational leaing in terms of both prediction accuracy and speed.In addition,SHC-EM is more robust and insusceptible to noise than variational leaing.