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\subsection{ARMA}  **Talk about using white noise to drive the process  There exist a class of finite difference equations used in the analysis of discrete time series known as autoregressive-moving average (ARMA) processes. These equations relate the autoregressive and moving average properties of a times series. (obvious) A stationary process $X_t$ $\{X_t\}$  is an ARMA(p,q) process if at every time $t$ $$X_t - \phi_1X_{t-1} - ... - \phi_pX_{t-p} = Z_t + \theta_{t-1} + ... + \theta_qZ_{t-q} $$  where ${Z_t}$ $\{Z_t\}$  is a white noise process with zero mean and variance $\sigma^2$. \!\!BE SURE TO CITE BROCKWELL AND DEVID TIME SERIES THEORY AND METHODS  **Quick intro to ARMA (an maybe a bit of stochastic analysis)