arXiv Machine Learning
Forecasting from Counterfactual Simulator Rollouts: A Sim2Real Evaluation
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Pseudo-Label-Triggered Retraining from Forecast Errors for Online Time Series Forecasting
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When to Retrain: An Empirical Study of Retraining Policies for Streaming ML Under Concept Drift, Budget, and Latency Constraints
arXiv:2608. 19488v1 Announce Type: new Abstract: Production machine learning systems degrade under concept drift, yet practitioners have little principled guidance on when to retrain.
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Systematic Evaluation of TabPFN-TS for Zero-Shot Probabilistic Heat Load Forecasting in District Heating Networks
arXiv:2608. 20024v1 Announce Type: new Abstract: District heating energy hubs require reliable heat load forecasts for efficient operational scheduling.
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Accuracy Is Not Service: A Decision-Aware Benchmark for Intermittent-Demand Forecasting
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Systematic Evaluation of TabPFN-TS for Zero-Shot Probabilistic Heat Load Forecasting in District Heating Networks
District heating energy hubs require reliable heat load forecasts for efficient operational scheduling. Conventional forecasting workflows train system-specific models on historical data, which can become burdensome when networks change through new consumers, retrofits, or changing operating regimes.
arXiv Machine Learning
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When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting
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