Time series prediction using Lyapunov exponents in embedding phase space

2002 ◽  
Author(s):  
Jun Zhang ◽  
K.F. Man ◽  
J.Y. Ke
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Lyapunov Exponents ◽  
Time Series Prediction

Time series prediction using Lyapunov exponents in embedding phase space

2004 ◽  
Vol 30 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Jun Zhang ◽  
K.C. Lam ◽  
W.J. Yan ◽  
Hang Gao ◽  
Yuan Li
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Lyapunov Exponents ◽  
Time Series Prediction

scholarly journals Multivariate time series prediction of high dimensional data based on deep reinforcement learning

2021 ◽  
Vol 256 ◽  
pp. 02038
Author(s):  
Xin Ji ◽  
Haifeng Zhang ◽  
Jianfang Li ◽  
Xiaolong Zhao ◽  
Shouchao Li ◽  
...  
Keyword(s):  
Time Series ◽  
Reinforcement Learning ◽  
Phase Space ◽  
Time Series Prediction ◽  
Multivariate Time Series ◽  
High Dimensional Data ◽  
Prediction Method ◽  
Conditional Entropy ◽  
High Dimensional ◽  
Low Dimensional

In order to improve the prediction accuracy of high-dimensional data time series, a high-dimensional data multivariate time series prediction method based on deep reinforcement learning is proposed. The deep reinforcement learning method is used to solve the time delay of each variable and mine the data characteristics. According to the principle of maximum conditional entropy, the embedding dimension of the phase space is expanded, and a multivariate time series model of high-dimensional data is constructed. Thus, the conversion of reconstructed coordinates from low-dimensional to high-dimensional can be kept relatively stable. The strong independence and low redundancy of the final reconstructed phase space construct an effective model input vector for multivariate time series forecasting. Numerical experiments of classical multivariable chaotic time series show that the method proposed in this paper has better forecasting effect, which shows the forecasting effectiveness of this method.

Chaotic time series prediction of E-nose sensor drift in embedded phase space

2013 ◽  
Vol 182 ◽  
pp. 71-79 ◽  
Author(s):  
Lei Zhang ◽  
Fengchun Tian ◽  
Shouqiong Liu ◽  
Lijun Dang ◽  
Xiongwei Peng ◽  
...  
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Time Series Prediction ◽  
Chaotic Time Series ◽  
Sensor Drift ◽  
Chaotic Time Series Prediction

Research on the Magnitude Time Series Prediction Based on Wavelet Neural Network

2011 ◽  
Vol 128-129 ◽  
pp. 233-236 ◽  
Author(s):  
Yan Lan Chen ◽  
Yi Chen ◽  
Qing Huang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Predictive Performance ◽  
Wavelet Neural Network ◽  
Single Step ◽  
Reconstructed Phase Space ◽  
Takens Theorem

Based on the fundamental principles of the wavelet analysis combining with BP neural network, the paper can obtain the minimum embedding dimension and delay time. According to the chaos theory, the phase space of the magnitude time series can be reconstructed by Takens theorem. The paper uses wavelet neural network to train and test the nonlinear magnitude time series in the reconstructed phase space. The simulation results show that the predictive effect of the magnitude time series is remarkable and the predictive performance of single-step prediction is superior to that of multi-step prediction.

scholarly journals Hidden Phase Space Reconstruction: A Novel Chaotic Time Series Prediction Method for Speech Signals

2018 ◽  
Vol 27 (6) ◽  
pp. 1221-1228 ◽  
Author(s):  
Jingjing Li ◽  
Qijin Zhang ◽  
Yumei Zhang ◽  
Xiaojun Wu ◽  
Xiaoming Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Phase Space Reconstruction ◽  
Chaotic Time Series ◽  
Speech Signals ◽  
Chaotic Time Series Prediction
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