arXiv:2512.07569v2 Announce Type: replace Abstract: Reliable forecasting of multivariate time series under anomalous conditions is crucial in applications such as ATM cash logistics, where sudden demand shifts can disrupt operations. Modern deep forecasters achieve high accuracy on normal data but often fail when distribution shifts occur. We propose Weighted Contrastive Adaptation (WECA), a Weighted contrastive objective that aligns normal and anomaly-augmented representations, preserving anomaly-relevant information while maintaining consistency under benign variations. Evaluations on a nati
The increasing complexity and unpredictability of real-world systems, coupled with growing reliance on AI for critical forecasting, necessitates more robust anomaly-aware methods.
This development addresses a key limitation of current deep learning models in handling distribution shifts, offering more reliable AI applications in sectors with high stakes.
AI-driven forecasting can become significantly more resilient to unexpected events and anomalies, reducing operational disruptions and improving critical decision-making.
- · Logistics and Supply Chain Operators
- · Financial Services
- · Deep Learning Researchers
- · Critical Infrastructure Management
- · Traditional Forecasting Models
- · Organizations Reliant on Unadaptable AI
Improved operational efficiency and reduced losses due to unexpected events in various industries.
Accelerated adoption of AI in risk-sensitive sectors where current models were deemed too fragile.
The development of hybrid AI systems that combine advanced forecasting with robust anomaly detection and response mechanisms becomes standard.
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