What kind of data does TS Correlation analyze?

TS Correlation specifically analyzes autocorrelation and cross-correlation of time series data. This type of analysis is essential in understanding the relationships between observations over time, which can exhibit patterns such as trends, seasonal variations, or cyclical behaviors. Autocorrelation measures how a variable correlates with itself across different time lags, while cross-correlation assesses the relationship between two different time series.

Understanding these correlations can help in building forecasting models and identifying potential predictive relationships between time-dependent observations, making it a key aspect of time series analysis. The focus on time series data distinguishes this from other forms of data analysis, such as regression or survival analysis, which deal with different statistical relationships and types of structures.