What is the difference between AR, MA, and ARIMA models?

AR uses past values, MA uses past errors, and ARIMA combines both with differencing.

AR models assume current value depends linearly on past values. MA models assume dependence on past prediction errors. ARIMA combines both and adds differencing to handle non-stationarity. This makes ARIMA more flexible for real-world time series where both signal and noise dependencies exist.