发表机构
Rutgers University; NEC Laboratories America（罗格斯大学; 美国NEC实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出多分辨率框架EventTime，结合多维度信息与动态对比目标，在自主构建的SECURE数据集上，实现了对网络安全事件后短期金融异常损失的更精准估计。
AI 中文摘要
通过网络传播的冲击（如网络安全漏洞披露）可能突然扰乱金融时间序列并造成重大异常损失。这些事件通过新闻报道、监管文件或公共数据库作为离散记录披露，但其后果通过持续的市场动态展开，由此产生了事件条件下的影响预测问题：给定事件前的市场历史和有限的事件元数据，目标是估计披露后的短期异常损失，而非重建完整的事件后轨迹。然而，大多数时间序列预测模型聚焦于趋势、季节性和自相关性等内生规律，难以应对罕见且异质的外部事件；稀疏的高影响事件和背景市场噪声进一步加剧了这一挑战。本文提出EventTime，这一多分辨率框架结合了长程市场上下文、短程事件前动态和事件元数据，包含将时间表示与事件属性耦合以识别相关近期市场模式的事件融合模块；为缓解稀疏监督问题，EventTime还引入了动态对比目标，在训练期间构建感知事件和时间序列的正、负样本对。此外，本文构建了SECURE这一真实世界数据集，将网络安全事件与股票市场时间序列及结构化、大语言模型（LLM）生成的语义特征对齐。实验表明，EventTime在估计事件后金融损失方面始终优于最先进的时间序列模型和事件感知基线；进一步分析显示，该模型能生成更具事件敏感性的表示、对不完整元数据的鲁棒性更强，且对网络安全披露后的短期市场影响的估计更具可解释性。
英文摘要
Shocks that spread through the web, such as cybersecurity breach disclosures, can abruptly disrupt financial time series and cause substantial abnormal losses. While these events are disclosed as discrete records through news reports, regulatory filings, or public databases, their consequences unfold through continuous market dynamics. This creates an event-conditioned impact prediction problem: given pre-event market history and limited event metadata, the goal is to estimate short-term post-disclosure abnormal loss rather than reconstruct the full post-event trajectory. However, most time-series forecasting models focus on endogenous regularities such as trend, seasonality, and autocorrelation, and thus struggle with rare and heterogeneous external events. The challenge is further amplified by sparse high-impact events and background market noise. We introduce EventTime, a multi-resolution framework that combines long-horizon market context, short-horizon pre-event dynamics, and event metadata. It incorporates an event fusion module that couples temporal representations with event attributes to identify relevant recent market patterns. To mitigate sparse supervision, EventTime further introduces a dynamic contrastive objective that constructs event- and time-series-aware positive and negative pairs during training. We also construct SECURE, a real-world dataset aligning cybersecurity incidents with stock-market time series and structured and LLM-derived semantic features. Experiments show that EventTime consistently outperforms state-of-the-art time-series and event-aware baselines in estimating post-event financial losses. Further analyses demonstrate more event-sensitive representations, greater robustness to incomplete metadata, and more interpretable estimates of short-term market impact following cybersecurity disclosures.
