  LSST is expected to yield ~10^7 light curves over the course of its mission,
which will require a concerted effort in automated classification. Stochastic
processes provide one means of quantitatively describing variability with the
potential advantage over simple light curve statistics that the parameters may
be physically meaningful. Here, we survey a large sample of periodic,
quasi-periodic, and stochastic OGLE-III variables using the damped random walk
(DRW, CARMA(1,0)) and quasi-periodic oscillation (QPO, CARMA(2,1)) stochastic
process models. The QPO model is described by an amplitude, a period, and a
coherence time-scale, while the DRW has only an amplitude and a time-scale. We
find that the periodic and quasi-periodic stellar variables are generally
better described by a QPO than a DRW, while quasars are better described by the
DRW model. There are ambiguities in interpreting the QPO coherence time due to
non-sinusoidal light curve shapes, signal-to-noise, error mischaracterizations,
and cadence. Higher-order implementations of the QPO model that better capture
light curve shapes are necessary for the coherence time to have its implied
physical meaning. Independent of physical meaning, the extra parameter of the
QPO model successfully distinguishes most of the classes of periodic and
quasi-periodic variables we consider.
