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This study systematically evaluates TabPFN-TS for zero-shot probabilistic heat load forecasting in district heating networks, comparing it with state-of-the-art time-series foundation models like Chronos-2 and machine-learning baselines.
TopoBrick is a training-free framework for zero-shot building IoT forecasting that uses building knowledge graphs and an agentic topology sampler to select target-specific exogenous variables. It outperforms strong zero-shot foundation-model baselines and remains competitive with fully trained building-specific models.
Darts, a popular open-source Python library for time series analysis, introduces a unified FoundationModel class collection that integrates multiple time series foundation models (Chronos-2, TimesFM 2.5, TiRex, PatchTST-FM) for zero-shot and fine-tuned forecasting with standardized interfaces and minimal dependencies.
This paper presents an applied evaluation of foundation models for time series forecasting compared to supervised approaches across four operational domains, and proposes a Complexity Router to selectively assign series to the optimal model class for balancing accuracy and inference cost.