We develop monthly asylum seeker flow forecasting models for 157 origin countries to the EU27, using machine learning and high-dimensional data, including digital trace data from Google Trends. Comparing different models and forecasting horizons and validating out-of-sample, we find that an ensemble forecast combining Random Forest and Extreme Gradient Boosting algorithms outperforms the random walk over horizons between 3 and 12 months. For large corridors, this holds in a parsimonious model exclusively based on Google Trends variables, which has the advantage of near real-time availability. We provide practical recommendations how our approach can enable ahead-of-period asylum seeker flow forecasting applications.

中文翻译：

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利用高维数据和机器学习技术预测双边寻求庇护者流动


我们使用机器学习和高维数据（包括来自 Google Trends 的数字追踪数据），为欧盟 27 国的 157 个原籍国开发每月寻求庇护者流量预测模型。比较不同的模型和预测范围并验证样本外，我们发现结合随机森林和极限梯度提升算法的集成预测优于 3 到 12 个月范围内的随机游走。对于大型走廊，这适用于完全基于 Google 趋势变量的简约模型，该模型具有近实时可用性的优势。我们提供实用建议，说明我们的方法如何实现提前寻求庇护者流量预测应用。