Generalization for Time Series in Tight Settings: Latency, Inference, Memory, prIvacy and Sustainability (TS-LIMITS)

The TS-LIMITS (Generalization for Time Series in Tight Settings: Latency, Inference, Memory, Privacy, and Sustainability) workshop addresses a critical yet underexplored challenge in machine learning: how to build time series models that generalize reliably under the real-world operational constraints that practitioners face every day. While much of the research community has focused on improving model accuracy, deployment in domains such as telecommunications, healthcare monitoring, industrial IoT, and autonomous systems demands that models also satisfy strict requirements on latency, inference efficiency, memory footprint, privacy preservation, and energy sustainability. TS-LIMITS brings together researchers and practitioners to share advances in areas including distribution shift, continual learning, federated and privacy-preserving learning, efficient architectures, and green AI, all through the lens of time series analysis. By fostering cross-disciplinary dialogue across these five tight constraints, the workshop aims to bridge the gap between academic research and real-world deployment, and to lay the groundwork for a more constraint-aware research agenda for time series generalization.