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An ARMA model combines autoregressive terms in past observations with moving-average terms in past errors within a single equation. The general equation is $Y_t = \omega + \phi_1 Y_{t-1} + \cdots + \phi_p Y_{t-p} + \theta_1 e_{t-1} + \cdots + \theta_q e_{t-q} + e_t$.

Conditions: Time-series modeling; Includes both lagged observations and lagged errors

An ARMA model combines autoregressive terms in past observations with moving-average terms in past errors within a single equation. The general equation is $Y_t = \omega + \phi_1 Y_{t-1} + \cdots + \phi_p Y_{t-p} + \theta_1 e_{t-1} + \cdots + \theta_q e_{t-q} + e_t$.

Conditions: Time-series modeling; Includes both lagged observations and lagged errors

AR models forecast a series based solely on past values of the series, called lags. MA models forecast a series based solely on past errors in the series, called error lags.

Conditions: Forecasting a time series; Uses past values of the same series for AR; Uses past errors for MA

AR models forecast a series based solely on past values of the series, called lags. MA models forecast a series based solely on past errors in the series, called error lags.

Conditions: Forecasting a time series; Uses past values of the same series for AR; Uses past errors for MA