Time series foundation models match the performance of specialized models for day-ahead load forecasting while providing explanations that match domain knowledge on weather and calendar effects.
Montero-Manso, R
2 Pith papers cite this work, alongside 20 external citations. Polarity classification is still indexing.
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Time series foundation models generate counterfactual forecasts showing increased transit ridership and reduced aggregate travel after NYC congestion pricing, with spatial and demographic heterogeneity.
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Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models
Time series foundation models match the performance of specialized models for day-ahead load forecasting while providing explanations that match domain knowledge on weather and calendar effects.
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Public transit gains and spatially uneven travel demand changes after NYC congestion pricing
Time series foundation models generate counterfactual forecasts showing increased transit ridership and reduced aggregate travel after NYC congestion pricing, with spatial and demographic heterogeneity.