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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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.
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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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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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.