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We describe a family of statistical tests to measure partial correlation invectorial timeseries. The test measures whether an observed timeseries Y can bepredicted from a second series X, even after accounting for a third series Zwhich may correlate with X. It does not make any assumptions on the nature ofthese timeseries, such as stationarity or linearity, but it does require thatmultiple statistically independent recordings of the 3 series are available.Intuitively, the test works by asking if the series Y recorded on oneexperiment can be better predicted from X recorded on the same experiment thanon a different experiment, after accounting for the prediction from Z recordedon both experiments.

A test for partial correlation between repeatedly observed nonstationary nonlinear timeseries