  Quantifying synchronization phenomena based on the timing of events has
recently attracted a great deal of interest in various disciplines such as
neuroscience or climatology. A multitude of similarity measures has been
proposed for this purpose, including Event Synchronization (ES) and Event
Coincidence Analysis (ECA) as two widely applicable examples. While ES defines
synchrony in a data adaptive local way that does not distinguish between
different time scales, ECA requires selecting a specific scale for analysis. In
this paper, we use slightly modified versions of both ES and ECA that address
previous issues with respect to proper normalization and boundary treatment,
which are particularly relevant for short time series with low temporal
resolution. By numerically studying threshold crossing events in coupled
autoregressive processes, we identify a practical limitation of ES when
attempting to study synchrony between serially dependent event sequences
exhibiting event clustering in time. Practical implications of this observation
are demonstrated for the case of functional network representations of climate
extremes based on both ES and ECA, while no marked differences between both
measures are observed for the case of epileptic electroencephalogram (EEG)
data. Our findings suggest that careful event detection along with diligent
preprocessing is recommended when applying ES while less crucial for ECA.
Despite the lack of a general modus operandi for both event definition and
detection of synchronization, we suggest ECA as a widely robust method,
especially for time resolved synchronization analyses of event time series from
various disciplines.
