  While it is tempting in experimental practice to seek as high a data rate as
possible, oversampling can become an issue if one takes measurements too
densely. These effects can take many forms, some of which are easy to detect:
e.g., when the data sequence contains multiple copies of the same measured
value. In other situations, as when there is mixing$-$in the measurement
apparatus and/or the system itself$-$oversampling effects can be harder to
detect. We propose a novel, model-free technique to detect local mixing in time
series using an information-theoretic technique called permutation entropy. By
varying the temporal resolution of the calculation and analyzing the patterns
in the results, we can determine whether the data are mixed locally, and on
what scale. This can be used by practitioners to choose appropriate lower
bounds on scales at which to measure or report data. After validating this
technique on several synthetic examples, we demonstrate its effectiveness on
data from a chemistry experiment, methane records from Mauna Loa, and an
Antarctic ice core.
