2021/12/09 by Tobias Braun, Cinthya N. Fernandez, Deniz Eroglu +3
Computer Science · Earth and Planetary Sciences · Environmental Science · Mathematics · Physics and Astronomy · #Climate variability and models #Metric (unit) #Nonlinear system #Predictability #Proxy (statistics) #Sampling (signal processing) #Series (stratigraphy) #Spurious relationship #Time Series Analysis and Forecasting #Time series #Tree-ring climate responses #nlin.CD #stat.ME
paper · pdf · doi:10.1103/physreve.105.024206
arxiv created 2021/12/09 · openalex created_date 2021/12/31 · openalex publication_date 2022/02/28 · arxiv updated 2022/03/14 · openalex updated_date 2026/08/05
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.