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where $\{Z_t\}$ is a white noise process with zero mean and variance $\sigma^2$. For any autocovariance function $\gamma(h)$ for which $\lim_{h\to\infty}\gamma(h) = 0$ and any integer $k > 0$, there exists an ARMA(p,q) process with autocovariance function $\gamma_X(h)$ such that $\gamma_X(h) = \gamma(h)$ for all $h \leq k$. This makes the ARMA process a very useful tool in the analysis and modeling of many different time-series. (reword this blurb, too much directly from the text) Unfortunately, the phenomena we are observing are not discrete processes, so in order to get a proper understanding of the underlying physics, we need a continuous analog to the ARMA process.  \!\!BE SURE TO CITE BROCKWELL AND DEVID DAVIS  TIME SERIES THEORY AND METHODS %**Quick intro to ARMA (an maybe a bit of stochastic analysis)  %***Background, current use