Cross-correlation is a statistical technique used to measure the similarity between two time series data sets. This method calculates the correlation between the two data sets at different time lags, or offsets, to determine if there is a relationship between the two variables. Cross-correlation is commonly used in various fields such as signal processing, economics, and finance to identify patterns, relationships, and dependencies between different sets of data. It can help researchers understand the dynamics and interactions between variables and can be a powerful tool for analyzing complex data sets.