A fine-tuned 1.7B language model routes time series forecasting ensembles by reasoning over hybrid text-number series features and retrieved similar cases, beating fixed and learned ensemble baselines on eight benchmarks.
Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets.Statistics in medicine, 34(1):106–117, 2015
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REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting
A fine-tuned 1.7B language model routes time series forecasting ensembles by reasoning over hybrid text-number series features and retrieved similar cases, beating fixed and learned ensemble baselines on eight benchmarks.