Sampling rate-corrected analysis of irregularly sampled time series

TL;DR

This paper introduces a correction method for analyzing irregularly sampled time series that accounts for sampling rate variations, improving the accuracy of recurrence analysis in detecting true regime shifts.

Contribution

It proposes sampling rate constrained surrogates and a correction scheme to mitigate biases caused by sampling rate variations in recurrence quantification analysis.

Findings

Corrects for sampling rate bias in recurrence analysis.

Uncovers true climate regime shifts in speleothem data.

Identifies spurious transitions caused by sampling artifacts.

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 non-trivial 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 novel 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.