  The refugee crisis is perhaps the single most challenging problem for Europe
today. Hundreds of thousands of people have already traveled across dangerous
sea passages from Turkish shores to Greek islands, resulting in thousands of
dead and missing, despite the best rescue efforts from both sides. One of the
main reasons is the total lack of any early warning-alerting system, which
could provide some preparation time for the prompt and effective deployment of
resources at the hot zones. This work is such an attempt for a systemic
analysis of the refugee influx in Greece, aiming at (a) the statistical and
signal-level characterization of the smuggling networks and (b) the formulation
and preliminary assessment of such models for predictive purposes, i.e., as the
basis of such an early warning-alerting protocol. To our knowledge, this is the
first-ever attempt to design such a system, since this refugee crisis itself
and its geographical properties are unique (intense event handling, little or
no warning). The analysis employs a wide range of statistical, signal-based and
matrix factorization (decomposition) techniques, including linear &
linear-cosine regression, spectral analysis, ARMA, SVD, Probabilistic PCA, ICA,
K-SVD for Dictionary Learning, as well as fractal dimension analysis. It is
established that the behavioral patterns of the smuggling networks closely
match (as expected) the regular burst and pause periods of store-and-forward
networks in digital communications. There are also major periodic trends in the
range of 6.2-6.5 days and strong correlations in lags of four or more days,
with distinct preference in the Sunday-Monday 48-hour time frame. These results
show that such models can be used successfully for short-term forecasting of
the influx intensity, producing an invaluable operational asset for planners,
decision-makers and first-responders.
