Multivariate chaotic time series prediction using multiple kernel extreme learning machine

Wang Xin-Ying; Han Min

doi:10.7498/aps.64.070504

Abstract

Multivariate chaotic time series is widely present in nature, such as in economy, society, industry and other fields. Modeling and predicting multivariate time series will help human to better manage, control, and make decision. A prediction method based on multiple kernel extreme learning machine is proposed in this paper to model the complex dynamics of multivariate chaotic time series. First, the multivariate chaotic time series is reconstructed in phase space, transforming the temporal correlation into spatial correlation. Then, a prediction model-multiple kernel extreme learning machine, which combines the multiple kernel learning and extreme learning machine with kernels, is proposed to approximate the nonlinear function of the input - output data in phase space. The proposed multiple kernel extreme learning machine could effectively combine the simple training of extreme learning machine with kernels and the data fusion capabilities of multiple kernel learning. Simulation results based on Lorenz chaotic time series prediction and San Francisco monthly runoff prediction demonstrate that, compared with other state-of-art chaotic time series prediction methods, the proposed multiple kernel extreme learning machine could get a better prediction accuracy.

Keywords:

Authors and contacts

1.
Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116023, China
Funds: Project supported by the National Natural Science Foundation of China (Grant No. 61374154), and the State Key Development Program for Basic Research of China (973 Program) (Grant No. 2013CB430403).

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Cited By

[1]	Deyle E, Sugihara G 2011 PLoS ONE 6 e18295
[2]	De Gooijer J G, Hyndman R J 2006 Int. J. Forecasting 22 443
[3]	Frenzel S, Pompe B 2007 Phys. Rev. Lett. 99 204101
[4]	Wang X Y, Han M 2012 Acta Phys. Sin. 61 80507 (in Chinese) [王新迎, 韩敏 2012 物理学报 61 80507]
[5]	Grigorievskiy A, Miche Y, Ventela A M, Severin E, Lendasse A 2014 Neural Netw. 51 50
[6]	Butcher J B, Verstraeten D, Schrauwen B, Day C R, Haycock P W 2013 Neural Netw. 38 76
[7]	Tang Z J, Ren F, Peng T, Wang W B 2014 Acta Phys. Sin. 63 50505 (in Chinese) [唐舟进, 任峰, 彭涛, 王文博 2014 物理学报 63 50505]
[8]	Sapankevych N, Sankar R 2009 IEEE Comput. Intell. M. 4 24
[9]	Huang G B, Zhou H M, Ding X J, Zhang R 2012 IEEE Trans. Syst. Man Cybern. B Cybern. 42 513
[10]	Huang G B, Zhu Q Y, Siew C K 2006 Neurocomputing 70 489
[11]	Wang H Q, Sun F C, Cai Y N, Chen N, Ding L G 2010 Acta Autom. Sin. 36 1037 (in Chinese) [汪洪桥, 孙富春, 蔡艳宁, 陈宁, 丁林阁 2010 自动化学报 36 1037]
[12]	Gönen M, Alpaydin E 2011 J. Mach. Learn. Res. 12 2211
[13]	Zhang J F, Hu S S 2008 Acta Phys. Sin. 57 2708 (in Chinese) [张军峰, 胡寿松 2008 物理学报 57 2708]
[14]	Rakotomamonjy A, Bach F, Canu S, Grandvalet Y 2008 J. Mach. Learn. Res. 9 2491
[15]	Takens F 1981 Dynam. Syst. Turbul. 898 366
[16]	Liang N Y, Huang G B, Saratchandran P, Sundararajan N 2006 IEEE Trans. Neurl Netw. 17 1411
[17]	Hipel K W, McLeod A I 1994 Time series modelling of water resources and environmental systems. (Amsterdam: Elsevier) p553

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Catalog

Vol. 64, Issue 7 - 2015-04-05

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