An autoregressive time series model is a linear model that predicts its current value using its most recent past value as the independent variable. An AR model of order p, denoted by AR(p) uses p lags of a time series to predict its current value.
The chain rule of forecasting is used to predict successive forecasts.
The one-period ahead forecast of from an AR(1) model is
can be used to forecast the two-period ahead value :