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Stationarity means that the statistical distribution of the data depends only on the difference in time between observations, rather than their absolute location in time.

Conditions: Applies to time series data.

Stationarity means that the statistical distribution of the data depends only on the difference in time between observations, rather than their absolute location in time.

Conditions: Applies to time series data.

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

Two methods are introduced for selecting model order: plotting patterns in correlation and using automatic selection techniques.

Conditions: Used when deciding how many AR and MA terms to include

Two methods are introduced for selecting model order: plotting patterns in correlation and using automatic selection techniques.

Conditions: Used when deciding how many AR and MA terms to include