  The effects of the so-called "refugee crisis" of 2015-16 continue to dominate
the political agenda in Europe. Migration flows were sudden and unexpected,
leaving governments unprepared and exposing significant shortcomings in the
field of migration forecasting. Migration is a complex system typified by
episodic variation, underpinned by causal factors that are interacting, highly
context dependent and short-lived. Correspondingly, migration monitoring relies
on scattered data, while approaches to forecasting focus on specific migration
flows and often have inconsistent results that are difficult to generalise at
the regional or global levels.
  Here we show that adaptive machine learning algorithms that integrate
official statistics and non-traditional data sources at scale can effectively
forecast asylum-related migration flows. We focus on asylum applications lodged
in countries of the European Union (EU) by nationals of all countries of origin
worldwide; the same approach can be applied in any context provided adequate
migration or asylum data are available.
  We exploit three tiers of data - geolocated events and internet searches in
countries of origin, detections of irregular crossings at the EU border, and
asylum recognition rates in countries of destination - to effectively forecast
individual asylum-migration flows up to four weeks ahead with high accuracy.
Uniquely, our approach a) monitors potential drivers of migration in countries
of origin to detect changes early onset; b) models individual
country-to-country migration flows separately and on moving time windows; c)
estimates the effects of individual drivers, including lagged effects; d)
provides forecasts of asylum applications up to four weeks ahead; e) assesses
how patterns of drivers shift over time to describe the functioning and change
of migration systems.
