scholarly journals Time dependent intrinsic correlation analysis of temperature and dissolved oxygen time series using empirical mode decomposition

2014 ◽  
Vol 130 ◽  
pp. 90-100 ◽  
Author(s):  
Yongxiang Huang ◽  
François G. Schmitt
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Dissolved Oxygen ◽  
Empirical Mode Decomposition ◽  
Time Dependent ◽  
Mode Decomposition ◽  
Time Dependent Intrinsic Correlation ◽  
Intrinsic Correlation

THE TIME-DEPENDENT INTRINSIC CORRELATION BASED ON THE EMPIRICAL MODE DECOMPOSITION

2010 ◽  
Vol 02 (02) ◽  
pp. 233-265 ◽  
Author(s):  
XIANYAO CHEN ◽  
ZHAOHUA WU ◽  
NORDEN E. HUANG
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Empirical Mode Decomposition ◽  
Sliding Window ◽  
Time Dependent ◽  
New Approach ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Time Dependent Intrinsic Correlation ◽  
Intrinsic Correlation

A Time-Dependent Intrinsic Correlation (TDIC) method is introduced. This new approach includes both auto- and cross-correlation analysis designed especially to analyze, capture and track the local correlations between nonlinear and nonstationary time series pairs. The approach is based on Empirical Mode Decomposition (EMD) to decompose the nonlinear and nonstationary data into their intrinsic mode functions (IMFs) and uses the instantaneous periods of the IMFs to determine a set of the sliding window sizes for the computation of the running correlation coefficients for multi-scale data. This new method treats the selection of the sliding window sizes as an adaptive process determined by the data itself, not a "tuning" process. Therefore, it gives an intrinsic correlation analysis of the data. Furthermore, the multi-window approach makes the new method applicable to complicated data from multi-scale phenomena. The synthetic and time series from real world are used to demonstrate conclusively that the new approach is far more superior over the traditional method in its ability to reveal detailed and subtle correlations unavailable through any other methods in existence. Thus, the TDIC represents a major advance in statistical analysis of data from nonlinear and nonstationary processes.

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