  We introduce and test several novel approaches for periodicity detection in
unevenly-spaced sparse datasets. Specifically, we examine five different kinds
of periodicity metrics, which are based on non-parametric measures of serial
dependence of the phase-folded data. We test the metrics through simulations in
which we assess their performance in various situations, including various
periodic signal shapes, different numbers of data points and different signal
to noise ratios. One of the periodicity metrics we introduce seems to perform
significantly better than the classical ones in some settings of interest to
astronomers. We suggest that this periodicity metric - the Hoeffding-test
periodicity metric - should be used in addition to the traditional methods, to
increase periodicity detection probability.
