An Autoregressive Model (AR model) is a statistical tool used for analyzing time series data. It predicts future values based on past observations, assuming that current values are influenced by their previous values. The model uses a linear combination of past data points to make forecasts, making it useful in various fields like economics and finance. In an AR model, the order of the model, denoted as p, indicates how many past values are considered. For example, an AR(1) model uses the immediately preceding value, while an AR(2) model incorporates the two most recent values. This approach helps capture trends and patterns in the data.