Cross-correlation is a statistical method used to measure the similarity between two signals or datasets. It involves comparing the values of one signal with the values of another signal at different time lags.
Autocorrelation measures the similarity between a signal and a lagged version of itself, while cross-correlation compares two different signals. Additionally, autocorrelation is used to detect patterns within a single signal, while cross-correlation is used to identify relationships between two signals.
Cross-correlation is commonly used in fields such as signal processing, time series analysis, and image processing. It can be used to identify patterns and trends in data, detect similarities between different signals, and remove noise from signals.
The cross-correlation between two signals is calculated by multiplying each data point in one signal by the corresponding data point in the other signal, and then summing these products. The resulting value represents the degree of similarity between the two signals at a specific time lag.
One limitation of cross-correlation is that it can only detect linear relationships between signals. It may also be affected by noise and outliers in the data. Additionally, cross-correlation does not indicate causation between two signals, only a relationship.