Autocorrelation refers to the degree of correlation between values of a time series data with itself at different points in time. In other words, it measures the relationship between consecutive observations in a series. Autocorrelation is commonly used in time series analysis to detect patterns, trends, and seasonality within a dataset. It is calculated by comparing the values of a variable with their own lagged values, typically using a correlation coefficient or autocorrelation function. Autocorrelation is important for understanding the behavior and predictability of time series data in various fields including economics, finance, and weather forecasting.