Chaotic Time Series Prediction Based on Gaussian Processes Mixture

Authors

Zhenjie Feng, 1. School of Computer and Information Engineering, Anyang Normal University, Anyang 455000, China; ;
Fan Yu, 2. Weinan Meteorological Bureau, Weinan 714000, China;

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.

Recommended Citation

Feng, Zhenjie and Yu, Fan (2019) "Chaotic Time Series Prediction Based on Gaussian Processes Mixture," Journal of System Simulation: Vol. 31: Iss. 7, Article 16.
DOI: 10.16182/j.issn1004731x.joss.18-0543
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss7/16

First Page

1387

Revised Date

2018-12-24

DOI Link

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

Last Page

1396

CLC

TM714

Recommended Citation

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

DOI

10.16182/j.issn1004731x.joss.18-0543