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Autocorrelation , also known as serial correlation or lagged correlation, is a statistical measure that describes the degree to which a time series (a sequence of data points measured at successive points in time) is correlated with itself at different time lags. In other words, it quantifies the relationship between a time series and a delayed (lagged) version of itself. Autocorrelation is a fundamental concept in time series analysis and has several important applications, including: 1. Identifying Patterns : Autocorrelation can reveal underlying patterns or trends in time series data. For example, it can help identify whether data exhibits seasonality (repeating patterns at fixed time intervals) or trend (systematic upward or downward movement). 2. Forecasting : Autocorrelation is used in autoregressive (AR) models, where the current value of a time series is modeled as a linear combination of its past values. The autocorrelation function helps determine the order of the AR model. 3...