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Phase Dispersion Minimization (PDM) is a data analysis technique that searches for periodic components of a time series data set. It is useful for data sets with gaps, non-sinusoidal variations, poor time coverage or other problems that would make Fourier techniques unuseable. It was first developed in 1978 and has been widely used for astronomical and other types of periodic data analyses.

Background
PDM is a variant of a standard astronomical technique called "data folding". This involves guessing a period for the data, and cutting, or "folding" the data into multiple sub-series with a time duration equal to the trial period. The data is now plotted versus "phase", or a scale lof 0->1, relative to the trial period. If the data is truly periodic with this period, a clean functional variation, or "light curve" will emerge. If not, the points will be randomly distributed in amplitude.

PDM Analysis
The PDM analysis divides the folded data into a series of bins and computes the variance of the amplitude within each bin. These bin variances are then combined and compared to the overall variance of the data set. For a true period the ratio of the bin to the total variances will be small. For a false period the ratio will be approximately unity. A plot of this ratio versus trial period will usually indicate the best candidates for periodic components.