Paleoclimate time series are oftentimes sparse and heterogeneously
sampled. Furthermore, time itself is a variable that has to be
reconstructed prior to analysis, which results in additional -- and
substantial -- uncertainties. Statistical analysis of such time series
usually dictates, that they be sampled regularly, a requirement often
met by means of linear interpolation. Such interpolation is,
... [Show full abstract] however,
immediately linked with loss of information on short timescales, and
over-estimation of variability on long timescales. We adapted similarity
estimators for Pearson correlation, mutual information and event
synchronization that do not require the time series to be sampled
regularly. We performed benchmark tests on synthetic data to infer,
which estimators are most robust in the presence of irregular sampling,
and how they are influenced by additional uncertainty on the time axis.
We compared results for standard estimators, using interpolated time
series, and the adapted estimators. We observe that interpolation of the
time series results in the largest estimation error for cross
correlation estimation, while the event synchronization function and
Gaussian-kernel-based correlation estimation show the overall lowest
error.