用滞后信号提升流感大爆发预测，效果显著优于现有方法。
CrossLag: Predicting Major Dengue Outbreaks with a Domain Knowledge Informed Transformer
- 引入环境滞后信号增强Transformer，低参数实现关键信息捕捉
- 在新加坡24周预测中，大幅提高重大疫情检测准确率
- 适合公共卫生预警与气候相关疾病预测研究者使用
尽管已有多种模型用于登革热病例预测，但及时预警重大疫情仍具挑战。本文提出CrossLag，一种融入环境滞后信号的注意力机制，将外生数据中重大事件后的滞后内生信号引入Transformer架构，参数量低。由于疫情通常滞后于气候与海洋异常变化，我们以TimeXer（一种区分外生-内生输入的通用Transformer）为基线。实验表明，在新加坡登革热数据上，跨24周预测窗口，所提模型在识别和预测重大疫情方面显著优于TimeXer。
原文摘要 · Abstract (English)
A variety of models have been developed to forecast dengue cases to date. However, it remains a challenge to predict major dengue outbreaks that need timely public warnings the most. In this paper, we introduce CrossLag, an environmentally informed attention that allows for the incorporation of lagging endogenous signals behind the significant events in the exogenous data into the architecture of the transformer at low parameter counts. Outbreaks typically lag behind major changes in climate and oceanic anomalies. We use TimeXer, a recent general-purpose transformer distinguishing exogenous-endogenous inputs, as the baseline for this study. Our proposed model outperforms TimeXer by a considerable margin in detecting and predicting major outbreaks in Singapore dengue data over a 24-week prediction window.
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