Andrei Velichko, Imen Mahmoud · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28563 · open-access PDF
オンライン検索活動は、流行の間に検索する情報のデジタルトラックとして広く使用されるが、その予測値はしばしば関連性、選択された遅延、または単一の予測エラースコアに減らされる。我々は、合致したLightGBM予測を用いて、両方向で8つの事前指定のGoogle Trends-COVID-19のカップルを調べる。
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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