## 5 Conclusions  
A more energy-concentrated TF result denotes a better TF location and better characterization of time-varying features. In this paper, a time series decomposition algorithm derived from the SSST was proposed, and TF correlation was analyzed in the TF plane. The local correlation between the signals in the TF domain were attained, and the correlations varying with time and frequency were able to reveal more specific and essential correlations between the signals. By comparing the traditional correlation analysis with the proposed method using sinusoidal signals, sinusoidal signals with noise, and non-stationary signals, the superiority of the proposed method is that the correlation can be obtained more intuitively and accurately. Similar to the traditional method, the SSST-TD TF correlation method is also affected by noise. The influence of noise must be further studied. As an extension to the traditional correlation analysis, the proposed method not only obtains the correlation between the signals in accordance with the traditional correlation analysis method, but also obtains the time-frequency correlation distribution and can be used for non-stationary signals. Thus, the following major conclusions can be drawn.  
1. For stationary signals, both the traditional correlation and the SSST-TD TF correlation methods can identify the correlations between signals, and the SSST-TD TF correlation method can also obtain the correlation as it changes with time and frequency.
2. Noise and the phase will affect the correlation coefficient. The traditional correlation method cannot recognize the influence of the phase, but the SSST-TD TF correlation method can get the effect of the phase of the cosine signals.
3. For time-varying signals, the correlation between the signals cannot be recognized by the traditional correlation method. The SSST-TD TF correlation method can obtain the correlation of non-stationary signals and can also reflect the time-varying and frequency-varying characteristics between the signals.  
Figure 20: Correlation coefficient between \(x\) and \(y\) at frequencies of (**A**) 86.25 Hz and (**B**) 173.75 Hz.  
**Author Contributions:** Conceptualization, G.P. and S.L.; Data curation, Y.Z.; Formal analysis, G.P.; Project administration, S.L.; and Software, G.P. and Y.Z.  
**Funding:** The research was funded by the National Natural Science Foundation of China (51675262), the Advance Research Field Fund Project of China (6140210020102), the Fundamental Research Funds for the Central Universities (NP2018304), and the Major National Science and Technology Projects (2017-IV-0008-0045).  
**Acknowledgments:** The authors wish to thank [PERSON] for his MATLAB Synchrosqueezing Toolbox, and three anonymous reviewers for their constructive comments and suggestions.  
**Conflicts of Interest:** No conflicts of interest exist in the submission of this manuscript, and the manuscript has been approved by all the authors for publication. The work described herein is original research that has not been published previously and is not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.