  We explore methods for the identification of stellar flare events in
irregularly sampled data of ground-based time domain surveys. In particular, we
describe a new technique for identifying flaring stars, which we have
implemented in a publicly available Python module called "PyVAN". The approach
uses the Differential Evolution algorithm to optimize parameters of empirically
derived light-curve templates for different types of stars to fit a candidate
light-curve. The difference of the likelihoods that these best-fit templates
produced the observed data is then used to delineate targets that are well
explained by a flare template but simultaneously poorly explained by templates
of common contaminants. By testing on light-curves of known identity and
morphology, we show that our technique is capable of recovering flaring status
in $69\%$ of all light-curves containing a flare event above thresholds drawn
to include $\lt1\%$ of any contaminant population. By applying to Palomar
Transient Factory data, we show consistency with prior samples of flaring
stars, and identify a small selection of candidate flaring G-type stars for
possible follow-up.
