AR models are a very useful tool to tackle the prediction problem in relation to a time series. A strong correlation between consecutive values of a series is often observed. In this case, we speak of autocorrelation of the first order when we consider adjacent values, of the second order if we refer to the relation between the values of the series after two periods, and in general of the pth order if the values considered have p periods between them. AR models allow exploiting these bonds to obtain useful forecasts of the future behavior of the series.
AR is a linear predictive modeling technique. This model tries to predict the time series based on the previous values assumed using the AR parameters as coefficients. ...
