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This paper systematically evaluates time series foundation models (TSFMs) for forecasting extreme PM2.5 concentrations from wildfire smoke using a 12-year dataset from California. Results show that fully-trained recurrent baselines like BiLSTM outperform TSFMs, challenging the assumption that larger pretrained models dominate in environmental forecasting.
GlucoFM-Bench evaluates time-series foundation models for blood glucose forecasting across 15 datasets, showing strong zero-shot/few-shot transfer by Chronos-2 and TimesFM but superior performance of a lightweight LSTM when full training data is available.