Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting
Large \emph{Time Series Foundation Models} (TSFMs) demonstrate strong zero-shot forecasting capabilities across diverse domains. However, their application to regional climate forecasting faces practical challenges: model weights are often proprietary, local training records are limited, and computational budgets are constrained, making traditional fine-tuning approaches infeasible. To address these constraints, we introduce a lightweight, black-box adaptation framework (requiring no access to backbone parameters and no backbone fine-tuning) that enhances frozen TSFMs at inference time through two plug-and-play wrappers: \textbf{SMR\textsuperscript{2}} (Stationarity aware multi-resolution...
| Authors | Wentao Gao, Jiuyong Li, Lin Liu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 July 2026 |
| Last updated at source | 26 September 2026 |
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