Abstract: While photometric surveys of normal stars for exoplanet transits have revealed many candidate planets, the effort is limited by non-Gaussian noise. This typically arises from stellar magnetic activity for space-based missions (Kepler, Corot, TESS) and from atmospheric effects for ground-based projects (WASP, HAT, AST3-1). Most treatments of these extraneous noise use nonparametric techniques, but we have found that parametric autoregressive ARIMA models are often very effective. This talk reviews the fitting of ARIMA models to reduce autocorrelated noise, the development of a Transit Comb Filter to find periodic transits in the ARIMA residuals, and application of a Random Forest classifier to reduce False Positives. Application to the Kepler mission data set discovers several dozen new planetary candidates. Preliminary examination indicates the method should allow planet detection for densely-cadenced ground-based surveys like HAT-S and AST3-1. ARIMA modeling may also valuable for studies of autocorrelated astrophysical phenomena like stellar activity and systems where the light is dominated by accretion onto compact objects.