Autoregressive terms are components of a time series model where the current value of a variable is regressed on its past values. This concept is crucial in capturing the relationship between an observation and a number of lagged observations, helping to understand trends and patterns in time-dependent data. Autoregressive terms allow for the incorporation of past information into predictive models, making them essential for both regression with time series data and mixed ARMA models.
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