Fully trained BiLSTM outperforms zero-shot and LoRA-adapted TSFMs on California wildfire PM2.5 under leave-one-incident-out evaluation, especially at hazardous AQI thresholds.
Evaluating deep learning time series models for PM2.5 forecasting across diverse horizons.iScience, 29(2): 114770, 2026
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Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5
Fully trained BiLSTM outperforms zero-shot and LoRA-adapted TSFMs on California wildfire PM2.5 under leave-one-incident-out evaluation, especially at hazardous AQI thresholds.