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Abstract:
The analysis of irregularly sampled time series remains a challenging task requiring methods that account
for continuous and abrupt changes of sampling resolution without introducing additional biases. The edit
distance is an effective metric to quantitatively compare time series segments of unequal length by computing
the cost of transforming one segment into the other. We show that transformation costs generally exhibit a
nontrivial relationship with local sampling rate. If the sampling resolution undergoes strong variations, this
effect impedes unbiased comparison between different time episodes. We study the impact of this effect on
recurrence quantification analysis, a framework that is well suited for identifying regime shifts in nonlinear time
series. A constrained randomization approach is put forward to correct for the biased recurrence quantification measures. This strategy involves the generation of a type of time series and time axis surrogates which we call
sampling-rate-constrained (SRC) surrogates. We demonstrate the effectiveness of the proposed approach with a
synthetic example and an irregularly sampled speleothem proxy record from Niue island in the central tropical
Pacific. Application of the proposed correction scheme identifies a spurious transition that is solely imposed by
an abrupt shift in sampling rate and uncovers periods of reduced seasonal rainfall predictability associated with enhanced ENSO and tropical cyclone activity.
