Chaotic Time Series Prediction Based on Gaussian Processes Mixture

doi:10.16182/j.issn1004731x.joss.18-0543

Abstract

Abstract: Aiming at the problem that the existing learning algorithms of Gaussian processes mixture (GPM) model, such as Markov Chain Monte Carlo (MCMC), variation or leave one out, have high computational complexity, a hidden variables posterior hard-cut iterative training algorithm is proposed, which simplifies the training process of the model. The GPM model based on the proposed algorithm is applied to chaotic time series prediction. The effects of embedding dimension, time delay, learning sample number, and testing sample numbers on predictive ability are discussed. It is demonstrated by the experimental results that the prediction of the GPM model is more accurate than SVM, GP and RBF network, and the training speed of GPM model falls in between RBF network, GP model, and SVM.

Key words: Gaussian processes mixtures model, chaotic time series, prediction, machine learning

CLC Number:

TM714

Feng Zhenjie, Fan Yu. Chaotic Time Series Prediction Based on Gaussian Processes Mixture[J]. Journal of System Simulation, 2019, 31(7): 1387-1396.

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