Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting

arXiv:2607.17511 · cs.LG · Submitted 2026-07-20 · Read on arXiv

cs.LG

Submitted: 2026-07-20

Updated: 2026-09-26

Code: https://github.com/thuml/Time-SeriesLibrary

Terminology

• Foundation Model — 41× in this paper · explained in 3 episodes
• Foundation Models — 27× in this paper · explained in 7 episodes
• Adaptation — 17× in this paper · explained in 1 episode
• Calibration — 12× in this paper · explained in 7 episodes
• Fine-Tuning — 12× in this paper · explained in 5 episodes
• Transformer — 12× in this paper · explained in 2 episodes
• Time Series Forecasting — 10× in this paper · explained in 3 episodes
• Moving Block Bootstrap — 9× in this paper
• Mean Squared Error — 6× in this paper
• Standardized Precipitation Evapotranspiration Index — 5× in this paper
• Ablation — 4× in this paper · explained in 1 episode
• Generalization — 4× in this paper · explained in 13 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Time Series Foundation Models (TSFMs) — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Embeddings — 1× in this paper · explained in 2 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Mean Absolute Error — 1× in this paper
• Two-Phase Framework — 1× in this paper · explained in 2 episodes

Sources

• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• On the Opportunities and Risks of Foundation Models
• TimeGPT-1
• From Tables to Time: Extending TabPFN-v2 to Time Series Forecasting
• Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

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