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The paper compares foundation models with market-specific benchmarks for electricity price forecasting, finding that only TabPFN consistently outperforms statistically, but economic value varies by strategy and risk tolerance, suggesting foundation models cannot universally replace tailored models.
This paper introduces NOMADD, a post-hoc method to reduce concept drift in tabular models by fitting base models on labeled periods and extrapolating compressed parameter changes. It achieves competitive performance with Drift-Resilient TabPFN at a fraction of training and inference cost.