arXiv:2606.02138v1 Announce Type: new Abstract: Out of distribution (OOD) events in multivariate time series forecasting are rare but often dominate real world risk, making average case forecasting insufficient for reliable deployment. Under standard average risk training on mixed ID/OOD distributions, optimization signals from rare OOD events can be overwhelmed by frequent in distribution (ID) patterns, so strong benchmark accuracy may not translate into reliability under high impact shifts. To address this issue, we propose VLBM (Variational Latent Basis Model), a theory guided latent foreca
The increasing deployment of AI in critical real-world systems necessitates robust forecasting methodologies that can reliably handle unexpected events, moving beyond average-case performance.
Reliable out-of-distribution (OOD) forecasting is crucial for AI deployment in high-stakes environments, where rare but impactful events can lead to significant real-world risks if not accounted for.
This research introduces a novel approach to multivariate time series forecasting that explicitly optimizes for OOD robustness, promising more reliable and trustworthy AI for critical applications.
- · AI Safety Researchers
- · High-reliability Systems Manufacturers
- · Financial Risk Management
- · Critical Infrastructure Operators
- · AI systems focused solely on average-case accuracy
- · Traditional time series forecasting methods
Improved reliability of AI forecasts in unpredictable real-world scenarios, reducing unexpected failures.
Increased adoption of AI in risk-sensitive sectors like finance, defense, and healthcare due to enhanced trustworthiness.
New regulatory frameworks for AI deployment that mandate demonstrable OOD robustness, shifting industry standards.
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Read at arXiv cs.LG