Comparison using cross correlation [closed]

1. How do you interpret cross-correlation results?
2. How do you compare similarity between two signals?
3. What is cross-correlation used for?
4. What is a good cross-correlation value?

How do you interpret cross-correlation results?

If the slope is positive, the cross correlation is positive; if there is a negative slope, the cross correlation is negative. This helps to identify important lags (or leads) in the process and is useful for application when there are predictors in an ARIMA model.

How do you compare similarity between two signals?

For measuring the similarity between two temporal signals, you can try using Dynamic Time Warping (DTW). DTW constructs a distance matrix between the two signals and tries to find minimum distance the two signals. If the two signals are identical, then distance is zero.

What is cross-correlation used for?

Cross-correlation is used to evaluate the similarity between the spectra of two different systems, for example, a sample spectrum and a reference spectrum. This technique can be used for samples where background fluctuations exceed the spectral differences caused by changes in composition.

What is a good cross-correlation value?

Cross-correlation is generally used when measuring information between two different time series. The possible range for the correlation coefficient of the time series data is from -1.0 to +1.0. The closer the cross-correlation value is to 1, the more closely the sets are identical.