More capable LLMs produce worse distributional forecasts on superlinear growth time series with tail risks of regime change, with the error concentrated in the upper tail; this reverses on conventional threshold metrics.
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Regime-stratified evaluation on traffic benchmarks shows TSFM accuracy and interval coverage collapse during transitions (MAE 11 mph vs 3 mph overall; coverage to 55%), hidden by free-flow dominance, with BMA augmentation recovering transition coverage.
PULSE is a physics-informed plug-and-play framework that uses phase-anchored disentanglement, a Phase Router, and statistic-aware mixup to mitigate Phase Amnesia in non-stationary forecasting and achieve strong results with simple backbones.
Ensemble models using only satellite SST, upwelling, chlorophyll-a and PFTs predict Pseudo-nitzschia HAB occurrence at ROC–AUC 0.77±0.06 under year×cluster cross-validation on the Portuguese L1–L2 coast.
A frozen average of the last two cycles matches or exceeds eight shape-learning alternatives on 97 GIFT-Eval configurations for periodic time series forecasting.
citing papers explorer
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Is Capability a Liability? More Capable Language Models Make Worse Forecasts When It Matters Most
More capable LLMs produce worse distributional forecasts on superlinear growth time series with tail risks of regime change, with the error concentrated in the upper tail; this reverses on conventional threshold metrics.
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Do Time Series Foundation Model Benchmarks Hide Regime-Dependent Failures? Evidence from Traffic Speed Forecasting
Regime-stratified evaluation on traffic benchmarks shows TSFM accuracy and interval coverage collapse during transitions (MAE 11 mph vs 3 mph overall; coverage to 55%), hidden by free-flow dominance, with BMA augmentation recovering transition coverage.
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PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting
PULSE is a physics-informed plug-and-play framework that uses phase-anchored disentanglement, a Phase Router, and statistic-aware mixup to mitigate Phase Amnesia in non-stationary forecasting and achieve strong results with simple backbones.
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Predicting Pseudo-nitzschia harmful algal blooms along the Portuguese Coast using satellite-derived predictors
Ensemble models using only satellite SST, upwelling, chlorophyll-a and PFTs predict Pseudo-nitzschia HAB occurrence at ROC–AUC 0.77±0.06 under year×cluster cross-validation on the Portuguese L1–L2 coast.
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Don't Learn the Shape: Forecasting Periodic Time Series by Rank-1 Decomposition
A frozen average of the last two cycles matches or exceeds eight shape-learning alternatives on 97 GIFT-Eval configurations for periodic time series forecasting.