Time series are often “sticky” – a value will be correlated with the prior value, the one before that, and so on. The degree of correlation will typically decline the further back you go. AR models seek to use that auto-correlation information to produce forecasts.
Autoregressive
Autoregressive refers to time series forecasting models (AR models) in which the independent variables (predictors) are prior values of the time series itself.