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The NASA Transiting Exoplanet Survey Satellite (TESS) is observing tens ofmillions of stars with time spans ranging from $\sim$ 27 days to about 1 yearof continuous observations. This vast amount of data contains a wealth ofinformation for variability, exoplanet, and stellar astrophysics studies butrequires a number of processing steps before it can be fully utilized. In orderto efficiently process all the TESS data and make it available to the widerscientific community, the TESS Data for Asteroseismology working group, as partof the TESS Asteroseismic Science Consortium, has created an automatedopen-source processing pipeline to produce light curves corrected forsystematics from the short- and long-cadence raw photometry data and toclassify these according to stellar variability type. We will process all starsdown to a TESS magnitude of 15. This paper is the next in a series detailinghow the pipeline works. Here, we present our methodology for the automaticvariability classification of TESS photometry using an ensemble of supervisedlearners that are combined into a metaclassifier. We successfully validate ourmethod using a carefully constructed labelled sample of Kepler Q9 light curveswith a 27.4 days time span mimicking single-sector TESS observations, on whichwe obtain an overall accuracy of 94.9%. We demonstrate that our methodology cansuccessfully classify stars outside of our labeled sample by applying it to all$\sim$ 167,000 stars observed in Q9 of the Kepler space mission.

TESS Data for Asteroseismology (T'DA) Stellar Variability Classification Pipeline: Set-Up and Application to the Kepler Q9 Data