An autoregressive process is a statistical model used to describe a time series where the current value is influenced by its past values. This concept is central to understanding how past observations can predict future values, making it crucial in time series analysis. It highlights the dependency of a variable on its previous observations, which can be useful for forecasting and identifying patterns in data.
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