Autocorrelation is a statistical measure that evaluates the relationship between a variable and its past values over time. It helps identify patterns or trends within a dataset, indicating whether current values are influenced by previous ones. This concept is commonly used in time series analysis, where data points are collected at regular intervals. In practical terms, autocorrelation can reveal cycles or seasonal effects in data, such as weather patterns or stock prices. By analyzing autocorrelation, researchers and analysts can make better predictions and understand the underlying structure of the data they are studying.