  In many application domains, time series are monitored to detect extreme
events like technical faults, natural disasters, or disease outbreaks.
Unfortunately, it is often non-trivial to select both a time series that is
informative about events and a powerful detection algorithm: detection may fail
because the detection algorithm is not suitable, or because there is no shared
information between the time series and the events of interest. In this work,
we thus propose a non-parametric statistical test for shared information
between a time series and a series of observed events. Our test allows
identifying time series that carry information on event occurrences without
committing to a specific event detection methodology. In a nutshell, we test
for divergences of the value distributions of the time series at increasing
lags after event occurrences with a multiple two-sample testing approach. In
contrast to related tests, our approach is applicable for time series over
arbitrary domains, including multivariate numeric, strings or graphs. We
perform a large-scale simulation study to show that it outperforms or is on par
with related tests on our task for univariate time series. We also demonstrate
the real-world applicability of our approach on datasets from social media and
smart home environments.
