1School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401 2Weinan Meteorological Bureau, Weinan 714000 3Department of Statistical Science, Cornell University, Ithaca 14853, USA 4School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang 330013
Abstract：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 automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. (2) An effective sparse hard-cut expectation maximization (SHC-EM) learning algorithm for the GPM model is proposed to improve the prediction performance. SHC-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning 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 learning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.
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Ya-Tong Zhou, Yu Fan, Zi-Yi Chen, Jian-Cheng Sun. Multimodality Prediction of Chaotic Time Series with Sparse Hard-Cut EM Learning of the Gaussian Process Mixture Model. Chin. Phys. Lett., 2017, 34(5): 50502-.
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2017, Vol. 34 
