  Nearly one million light curves from the TESS Year 1 southern hemisphere
extracted from Full Frame Images with the DIAmante pipeline are processed
through the AutoRegressive Planet Search statistical procedure. ARIMA models
remove trends and lingering autocorrelated noise, the Transit Comb Filter
identifies the strongest periodic signal in the light curve, and a Random
Forest machine learning classifier is trained and applied to identify the best
potential candidates. Classifier training sets include injections of both
planetary transit signals and contaminating eclipsing binaries. The optimized
classifier has a True Positive Rate of 92.8% and a False Positive Rate of 0.37%
from the labeled training set. The result of this DIAmante TESS autoregressive
planet search (DTARPS) analysis is a list of 7,377 potential exoplanet
candidates. The classifier has a False Positive Rate of 0.3%, a 64% recall rate
for previously confirmed exoplanets, and a 78% negative recall rate for known
False Positives. The completeness map of the injected planetary signals shows
high recall rates for planets with 8 - 30 R(Earth) radii and periods 0.6-13
days and poor completeness for planets with radii < 2 R(Earth) or periods < 1
day. The list has many False Alarms and False Positives that need to be culled
with multifaceted vetting operations (Paper II).
