  The 4-year light curves of 156,717 stars observed with NASA's Kepler mission
are analyzed using the AutoRegressive Planet Search (ARPS) methodology
described by Caceres et al. (2019). The three stages of processing are: maximum
likelihood ARIMA modeling of the light curves to reduce stellar brightness
variations; constructing the Transit Comb Filter periodogram to identify
transit-like periodic dips in the ARIMA residuals; Random Forest classification
trained on Kepler Team confirmed planets using several dozen features from the
analysis. Orbital periods between 0.2 and 100 days are examined. The result is
a recovery of 76% of confirmed planets, 97% when period and transit depth
constraints are added. The classifier is then applied to the full Kepler
dataset; 1,004 previously noticed and 97 new stars have light curve criteria
consistent with the confirmed planets, after subjective vetting removes clear
False Alarms and False Positive cases. The 97 Kepler ARPS Candidate Transits
mostly have periods $P<10$ days; many are UltraShort Period hot planets with
radii $<1$% of the host star. Extensive tabular and graphical output from the
ARPS time series analysis is provided to assist in other research relating to
the Kepler sample.
