Autocorrelation is a statistical measure that evaluates how a variable correlates with itself over different time intervals. It helps identify patterns or trends in time series data, such as stock prices or weather patterns. A high autocorrelation indicates that past values strongly influence future values, while a low autocorrelation suggests randomness. In practical terms, autocorrelation can be used to detect seasonality or cyclic behavior in data. For example, economists might analyze economic indicators to forecast future trends based on historical patterns. Understanding autocorrelation is essential for effective data analysis and modeling in various fields.