Autoregressive Model

An Autoregressive (AR) model is a type of statistical model that predicts future values of a time series based on a linear combination of its own past values. The fundamental assumption of an AR model is that the current value of a variable is directly dependent on, and can be explained by, its preceding values plus a random error term. This "self-regressing" nature makes it particularly suitable for analyzing and forecasting data where temporal dependencies are significant and a variable's history provides strong predictive power for its future.