  Aperiodic variability is a characteristic feature of young stars, massive
stars, and active galactic nuclei. With the recent proliferation of time domain
surveys, it is increasingly essential to develop methods to quantify and
analyze aperiodic variability. We develop three timescale metrics that have
been little used in astronomy -- {\Delta}m-{\Delta}t plots, peak-finding, and
Gaussian process regression -- and present simulations comparing their
effectiveness across a range of aperiodic light curve shapes, characteristic
timescales, observing cadences, and signal to noise ratios. We find that
Gaussian process regression is easily confused by noise and by irregular
sampling, even when the model being fit reflects the process underlying the
light curve, but that {\Delta}m-{\Delta}t plots and peak-finding can coarsely
characterize timescales across a broad region of parameter space. We make
public the software we used for our simulations, both in the spirit of open
research and to allow others to carry out analogous simulations for their own
observing programs.
