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.
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