Pairwise correlation of signals

In summary, the conversation discusses different methods for detecting the correlation between input and output signals on a vehicle. The speaker initially tried using Pearson's correlation, but found it to be unreliable due to non-linear changes in the signals. They then discovered that distance correlation was more effective, but missed the sign of the correlation. The idea of combining distance correlation with the sign of Pearson's coefficient is proposed, but it is determined to not be a useful approach. The speaker ultimately decides to use only the distance correlation method due to its robustness in their specific case.
  • #1
serbring
271
2
Hi all,

On a vehicle I recorded an output signal that is positive and it's variability is lead to the 30 input signals, but not all together at the same insant. Just by checking the pairwise correlation between signals in a time periond, I'm able to detect which input signals lead to the variability of the output signal. At first I tried with Pearson's correlation, but the correlation might be non linear and not always the two signals change monotically. For these reasons this correlation coefficient is not very helpful. Then, I tried with distance correlation and it works very well, but I miss the sign of the correlation that is really important to me. So what about using distance correlation parameter and the sign of Pearson's coefficition to detect the sign of the correlation (i.e. negative or positive correlation)? Thus I may be able to detect if an increase or a decrease of an input signal lead to an increase of the output signal. Any comment is appreciated.

Thanks
 
  • #3
I don't think that gives a useful result. Consider cases where the distance correlation is large but the classical correlation is close to zero: the sign gets determined by random fluctuation, and you end up with either a large positive or a large negative value just by chance.

You can consider both values separately, if that helps, but mixing them that way leads to confusing results.
 
  • #4
Hi,

thanks for your reply, you're on right. I'll just use the distance correlation approach, I have seen that it is much more robust than Pearson's coefficient in my case.
 

Related to Pairwise correlation of signals

1. What is pairwise correlation of signals?

Pairwise correlation of signals is a statistical technique used to measure the degree of linear relationship between two signals. It involves calculating the correlation coefficient between two sets of data points, where a value close to 1 indicates a strong positive correlation, a value close to -1 indicates a strong negative correlation, and a value close to 0 indicates no correlation.

2. How is pairwise correlation of signals calculated?

The most commonly used method for calculating pairwise correlation of signals is Pearson's correlation coefficient, also known as the Pearson product-moment correlation coefficient. This involves dividing the covariance of the two signals by the product of their standard deviations. Other methods such as Spearman's rank correlation coefficient and Kendall's rank correlation coefficient can also be used depending on the nature of the data.

3. What is the purpose of pairwise correlation of signals?

Pairwise correlation of signals is used to determine the strength and direction of the relationship between two signals. It is commonly used in data analysis and research to identify patterns and trends, and to determine if there is a significant relationship between two variables. It can also be used to identify outliers or influential data points that may affect the overall correlation.

4. Can pairwise correlation of signals be used for non-linear relationships?

No, pairwise correlation of signals is only suitable for measuring linear relationships between two signals. If the relationship between the two signals is non-linear, other methods such as nonlinear regression or correlation analysis may be more appropriate. It is important to assess the nature of the relationship between the two signals before deciding on the appropriate method.

5. How can pairwise correlation of signals be interpreted?

The correlation coefficient obtained through pairwise correlation of signals can be interpreted as the strength and direction of the relationship between two signals. A value of 1 indicates a perfect positive correlation, a value of -1 indicates a perfect negative correlation, and a value of 0 indicates no correlation. The closer the value is to 0, the weaker the relationship between the two signals.

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