Quantifying Event Impacts on Time Series via Multiscale Contrastive Learning
EventTime is a multi-resolution framework that combines long-horizon market context, short-horizon pre-event dynamics, and event metadata to predict short-term post-disclosure abnormal financial losses An event fusion module couples temporal representations with event attributes to identify relevant recent market patterns affected by external shocks A dynamic contrastive objective constructs event- and time-series-aware positive and negative pairs during training to mitigate sparse supervision f
Analysis
TL;DR
- EventTime is a multi-resolution framework that combines long-horizon market context, short-horizon pre-event dynamics, and event metadata to predict short-term post-disclosure abnormal financial losses
- An event fusion module couples temporal representations with event attributes to identify relevant recent market patterns affected by external shocks
- A dynamic contrastive objective constructs event- and time-series-aware positive and negative pairs during training to mitigate sparse supervision from rare high-impact events
- The authors introduce SECURE, a real-world dataset aligning cybersecurity incidents with stock-market time series and both structured and LLM-derived semantic features
- EventTime consistently outperforms state-of-the-art time-series and event-aware baselines, showing greater robustness to incomplete metadata and more interpretable impact estimates
Why It Matters
This work addresses a critical gap in time-series forecasting: most models focus on endogenous patterns like trend and seasonality but fail to account for rare, heterogeneous external shocks such as cybersecurity breaches. For AI practitioners building financial risk systems, EventTime demonstrates how contrastive learning and multiscale architectures can effectively incorporate sparse event signals into continuous market dynamics, enabling more accurate and interpretable impact prediction.
Technical Details
- Multi-resolution architecture: EventTime operates across multiple temporal scales, integrating long-horizon market context with short-horizon pre-event dynamics and structured event metadata to capture both gradual trends and abrupt shock responses.
- Event fusion module: Couples learned temporal representations with event attributes (e.g., breach type, severity) to identify and weight relevant recent market patterns, enabling the model to distinguish event-driven signals from background noise.
- Dynamic contrastive learning objective: Constructs positive and negative pairs that are aware of both event semantics and time-series structure, addressing the fundamental challenge of sparse supervision caused by rare high-impact events.
- SECURE dataset: A novel real-world dataset aligning cybersecurity incidents with corresponding stock-market time series, enriched with both structured metadata and LLM-derived semantic features, providing a benchmark for event-conditioned impact prediction.
- Evaluation: Experiments demonstrate superior performance over state-of-the-art baselines in estimating post-event financial losses, with additional analyses confirming more event-sensitive representations and robustness to incomplete metadata.
Industry Insight
- Financial institutions and risk management teams should consider event-conditioned forecasting frameworks like EventTime for stress-testing portfolios against cyber risk and other exogenous shocks, rather than relying solely on traditional time-series models.
- The contrastive learning approach to sparse event supervision offers a transferable paradigm for other domains where high-impact events are rare but consequential, such as natural disasters, regulatory changes, or supply chain disruptions.
- The SECURE dataset and its LLM-derived semantic features highlight the growing importance of combining structured data with large language model embeddings for event characterization, suggesting that hybrid feature engineering will become a competitive advantage in financial AI applications.
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