Autoregressive Model
An autoregressive (AR) model is used to predict future values of a time series based on its past values. It assumes that the current value is influenced by previous observations. For example, an AR(1) model uses one previous value, while an AR(2) model uses two. The model is represented by the equation: Y_t = c + φ1 * Y_(t-1) + φ2 * Y_(t-2) + ... + φp * Y_(t-p) + ε_t, where Y_t is the current value, φ are the coefficients, and ε_t is the error term. AR models are valuable for forecasting by identifying patterns in historical data.
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