Detection of local mixing in time-series data using permutation entropy

Mixing of neighboring data points in a sequence is a common, but understudied, effect in physical experiments. This can occur in the measurement apparatus (if material from multiple time points is pulled into a measurement chamber simultaneously, for instance) or the system itself, e.g., via diffusion of isotopes in an ice sheet. We propose a model-free technique to detect this kind of local mixing in time-series data 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.