Application of decomposition-ensemble learning paradigm with phase space reconstruction for day-ahead PM 2.5 concentration forecasting

2017 ◽  
Vol 196 ◽  
pp. 110-118 ◽  
Author(s):  
Mingfei Niu ◽  
Kai Gan ◽  
Shaolong Sun ◽  
Fengying Li
Keyword(s):  
Phase Space ◽  
Ensemble Learning ◽  
Phase Space Reconstruction ◽  
Learning Paradigm ◽  
Pm 2.5

A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting

2012 ◽  
Vol 93 ◽  
pp. 432-443 ◽  
Author(s):  
Ling Tang ◽  
Lean Yu ◽  
Shuai Wang ◽  
Jianping Li ◽  
Shouyang Wang
Keyword(s):  
Energy Consumption ◽  
Ensemble Learning ◽  
Nuclear Energy ◽  
Learning Paradigm

A Study On Methods for Determining Phase Space Reconstruction Parameters

2021 ◽  
Author(s):  
Shihui Lang ◽  
Zhu Hua ◽  
Guodong Sun ◽  
Yu Jiang ◽  
Chunling Wei
Keyword(s):  
Time Delay ◽  
Phase Space ◽  
Correlation Dimension ◽  
Nearest Neighbor ◽  
Correlation Method ◽  
Phase Space Reconstruction ◽  
Lorenz Attractor ◽  
Embedding Dimension ◽  
Phase Trajectories ◽  
Auto Correlation

Abstract Several pairs of algorithms were used to determine the phase space reconstruction parameters to analyze the dynamic characteristics of chaotic time series. The reconstructed phase trajectories were compared with the original phase trajectories of the Lorenz attractor, Rössler attractor, and Chens attractor to obtain the optimum method for determining the phase space reconstruction parameters with high precision and efficiency. The research results show that the false nearest neighbor method and the complex auto-correlation method provided the best results. The saturated embedding dimension method based on the saturated correlation dimension method is proposed to calculate the time delay. Different time delays are obtained by changing the embedding dimension parameters of the complex auto-correlation method. The optimum time delay occurs at the point where the time delay is stable. The validity of the method is verified through combing the application of correlation dimension, showing that the proposed method is suitable for the effective determination of the phase space reconstruction parameters.

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