GUARD distills from multiple misaligned time-series foundation models into lightweight forecasters via instance-wise routing and uncertainty-gated temperature, reducing RMSE on meteorology, carbon flux, soil moisture, and energy tasks.
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A survey proposing a taxonomy of Injective, Bridging, and Internal Alignment paradigms to evolve TSA into user-driven Time Series Question Answering with LLMs.
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When to Trust, How to Distill: Multi-Foundation Model Guidance for Lightweight, Robust Scientific Time Series Forecasting
GUARD distills from multiple misaligned time-series foundation models into lightweight forecasters via instance-wise routing and uncertainty-gated temperature, reducing RMSE on meteorology, carbon flux, soil moisture, and energy tasks.