Andrei Velichko, Imen Mahmoud · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28563 · open-access PDF
在线搜索活动被广泛地用作在流行病期间寻找信息的数字痕迹,但它的预测值通常减少到相关性,选择的延迟或单一的预测错误分数。我们在两个方向中使用匹配的LightGBM预测,检查了八个预测趋势-COVID-19对,在滚动的400天窗口和1-14天视野中。
Machine translation (Meta M2M100); the English original follows.
Online search activity is widely used as a digital trace of information seeking during epidemics, but its predictive value is often reduced to correlation, a selected lag, or a single forecast-error score. We examine eight pre-specified Google Trends-COVID-19 pairs in both directions using matched LightGBM forecasts over rolling 400-day windows and horizons of 1-14 days. Log-RMSE gain is combined with forecast-error distribution entropy (FEDE), which captures horizon-dependent changes in the signed error distribution. The resulting directional predictive signature comprises response class, RMS
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