Multi-step prediction method for time series based on chaotic operator network

Xiu Chun-Bo; Xu Meng

doi:10.7498/aps.59.7650

Abstract

Combining the phase space reconstruction theory and the time series analysis theory, a prediction network applied to time series multi-step prediction is proposed. The network is constructed in the weight-sum form of some chaotic operators. Constant connections are adopted among the units. The control parameters of chaotic operators are adjusted by chaos optimization algorithm. The training samples, constructed by known time series data, are used in the training process only once, which makes the dynamic characteristics of the network change and tend to the predicted system with the lapse of time. The validity of the network can be proved by computing the Lyapunov exponent of prediction data. The multi-step predictions for engineering data are also realized by the method. Simulation results prove that the method could validly predict time series when the predictive step is not too long.

Keywords:

Authors and contacts

1.
School of Electrical Engineering and Automation, Tianjin Polytechnic University, Tianjin 300160, China

References

[1]	Yamin S A 2008 Economics Letters 101 6
[2]	Bec F, Alain G 2008 Journal of Econometrics 142 94
[3]	Michael J D, Martin S, Fabio S 2007 Journal of Econometrics 141 517
[4]	Wen X J 2007 Journal of System Simulation 19 1639 (in Chinese) [温晓君 2007 系统仿真学报 19 1639]
[5]	Zhang L Q, Shao C 2006 Journal of System Simulation 18 1593 (in Chinese)[张立权、邵诚 2006 系统仿真学报 18 1593]
[6]	Zhang Y, Guan W 2009 Acta Phys. Sin. 58 756 (in Chinese) [张勇、关伟 2009 58 756]
[7]	Zhang Q L, Wang S T 2009 Acta Phys. Sin. 58 107 (in Chinese) [张钦礼、王士同 2009 58 107]
[8]	Yang Y F, Ren X M, Qin W Y, Wu Y F, Zhi X Z 2008 Acta Phys. Sin. 57 6139 (in Chinese) [杨永锋、任兴民、秦卫阳、〖9] Wang G L, Yang P C, Mao Y Q 2008 Acta Phys. Sin. 57 714 (in Chinese)[王革丽、杨培才、毛宇清 2008 57 714]
[9]	Ma Q L, Zheng Q L, Peng H, Qin J W 2009 Acta Phys. Sin. 58 1410 (in Chinese) [马千里、郑启伦、彭宏、覃姜维 2009 58 1410]
[10]	Wang Y S, Sun J, Wang C J, Fan H D 2008 Acta Phys. Sin. 57 6120 (in Chinese)[王永生、孙瑾、王昌金、范洪达 2008 57 6120]
[11]	Zhang J F, Hu S S 2007 Acta Phys. Sin. 56 713 (in Chinese) [张军峰、胡寿松 2007 56 713]
[12]	He T, Zhou Z O 2007 Acta Phys. Sin. 56 693 (in Chinese) [贺涛、周正欧 2007 56 693]

Cited By

[1]	Yamin S A 2008 Economics Letters 101 6
[2]	Bec F, Alain G 2008 Journal of Econometrics 142 94
[3]	Michael J D, Martin S, Fabio S 2007 Journal of Econometrics 141 517
[4]	Wen X J 2007 Journal of System Simulation 19 1639 (in Chinese) [温晓君 2007 系统仿真学报 19 1639]
[5]	Zhang L Q, Shao C 2006 Journal of System Simulation 18 1593 (in Chinese)[张立权、邵诚 2006 系统仿真学报 18 1593]
[6]	Zhang Y, Guan W 2009 Acta Phys. Sin. 58 756 (in Chinese) [张勇、关伟 2009 58 756]
[7]	Zhang Q L, Wang S T 2009 Acta Phys. Sin. 58 107 (in Chinese) [张钦礼、王士同 2009 58 107]
[8]	Yang Y F, Ren X M, Qin W Y, Wu Y F, Zhi X Z 2008 Acta Phys. Sin. 57 6139 (in Chinese) [杨永锋、任兴民、秦卫阳、〖9] Wang G L, Yang P C, Mao Y Q 2008 Acta Phys. Sin. 57 714 (in Chinese)[王革丽、杨培才、毛宇清 2008 57 714]
[9]	Ma Q L, Zheng Q L, Peng H, Qin J W 2009 Acta Phys. Sin. 58 1410 (in Chinese) [马千里、郑启伦、彭宏、覃姜维 2009 58 1410]
[10]	Wang Y S, Sun J, Wang C J, Fan H D 2008 Acta Phys. Sin. 57 6120 (in Chinese)[王永生、孙瑾、王昌金、范洪达 2008 57 6120]
[11]	Zhang J F, Hu S S 2007 Acta Phys. Sin. 56 713 (in Chinese) [张军峰、胡寿松 2007 56 713]
[12]	He T, Zhou Z O 2007 Acta Phys. Sin. 56 693 (in Chinese) [贺涛、周正欧 2007 56 693]

[1]	Yan Yu-Wei, Jiang Yuan, Yang Song-Qing, Yu Rong-Bin, Hong Cheng. Network failure model based on time series. Acta Physica Sinica, 2022, 71(8): 088901. doi: 10.7498/aps.71.20212106
[2]	Li Jun, Li Da-Chao. Wind power time series prediction using optimized kernel extreme learning machine method. Acta Physica Sinica, 2016, 65(13): 130501. doi: 10.7498/aps.65.130501
[3]	Wei De-Zhi, Chen Fu-Ji, Zheng Xiao-Xue. Internet public opinion chaotic prediction based on chaos theory and the improved radial basis function in neural networks. Acta Physica Sinica, 2015, 64(11): 110503. doi: 10.7498/aps.64.110503
[4]	Tian Zhong-Da, Li Shu-Jiang, Wang Yan-Hong, Gao Xian-Wen. Chaotic characteristics analysis and prediction for short-term wind speed time series. Acta Physica Sinica, 2015, 64(3): 030506. doi: 10.7498/aps.64.030506
[5]	Song Tong, Li Han. Chaotic time series prediction based on wavelet echo state network. Acta Physica Sinica, 2012, 61(8): 080506. doi: 10.7498/aps.61.080506
[6]	Sheng Zheng. Research on different time-scale prediction models for the total electron content. Acta Physica Sinica, 2012, 61(21): 219401. doi: 10.7498/aps.61.219401
[7]	Yao Tian-Liang, Liu Hai-Feng, Xu Jian-Liang, Li Wei-Feng. Noise-level estimation of noisy chaotic time series based on the invariant of the largest Lyapunov exponent. Acta Physica Sinica, 2012, 61(6): 060503. doi: 10.7498/aps.61.060503
[8]	Li Jun, Zhang You-Peng. Single-step and multiple-step prediction of chaotic time series using Gaussian process model. Acta Physica Sinica, 2011, 60(7): 070513. doi: 10.7498/aps.60.070513
[9]	Wu Jian-Jun, Xu Shang-Yi, Sun Hui-Jun. Detrended fluctuation analysis of time series in mixed traffic flow. Acta Physica Sinica, 2011, 60(1): 019502. doi: 10.7498/aps.60.019502
[10]	Dong Zhao, Li Xiang. The study of network motifs induced from discrete time series. Acta Physica Sinica, 2010, 59(3): 1600-1607. doi: 10.7498/aps.59.1600
[11]	Song Qing-Song, Feng Zu-Ren, Li Ren-Hou. Multiple clusters echo state network for chaotic time series prediction. Acta Physica Sinica, 2009, 58(7): 5057-5064. doi: 10.7498/aps.58.5057
[12]	Du Jie, Cao Yi-Jia, Liu Zhi-Jian, Xu Li-Zhong, Jiang Quan-Yuan, Guo Chuang-Xin, Lu Jin-Gui. Local higher-order Volterra filter multi-step prediction model of chaotic time series. Acta Physica Sinica, 2009, 58(9): 5997-6005. doi: 10.7498/aps.58.5997
[13]	Yang Yong-Feng, Ren Xing-Min, Qin Wei-Yang, Wu Ya-Feng, Zhi Xi-Zhe. Prediction of chaotic time series based on EMD method. Acta Physica Sinica, 2008, 57(10): 6139-6144. doi: 10.7498/aps.57.6139
[14]	Wang Yong-Sheng, Sun Jin, Wang Chang-Jin, Fan Hong-Da. Prediction of the chaotic time series from parameter-varying systems using artificial neural networks. Acta Physica Sinica, 2008, 57(10): 6120-6131. doi: 10.7498/aps.57.6120
[15]	Wu Yan-Dong, Xie Hong-Bo. A new method to recognize determinism in time series. Acta Physica Sinica, 2007, 56(11): 6294-6300. doi: 10.7498/aps.56.6294
[16]	Meng Qing-Fang, Zhang Qiang, Mu Wen-Ying. A novel multi-step adaptive prediction method for chaotic time series. Acta Physica Sinica, 2006, 55(4): 1666-1671. doi: 10.7498/aps.55.1666
[17]	Wang Hong-Wei, Ma Guang-Fu. Prediction of chaotic time series based on fuzzy model. Acta Physica Sinica, 2004, 53(10): 3293-3297. doi: 10.7498/aps.53.3293
[18]	Tan Wen, Wang Yao-Nan, Zhou　Shao-Wu, Liu　Zu-Run. Prediction of the chaotic time series using neuro-fuzzy networks. Acta Physica Sinica, 2003, 52(4): 795-801. doi: 10.7498/aps.52.795
[19]	Gan Jian-Chao, Xiao Xian-Ci. Nonlinear adaptive multi-step-prediction of chaotic time series based on points in the neighborhood. Acta Physica Sinica, 2003, 52(12): 2995-3001. doi: 10.7498/aps.52.2995
[20]	Zhang Jiashu, Xiao Xianchi. . Acta Physica Sinica, 2000, 49(3): 403-408. doi: 10.7498/aps.49.403

Catalog

Vol. 59, Issue 11 - 2010-06-05

Metrics

Abstract views: 8307
PDF Downloads: 1001
Cited By: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

Search

Article

留言板