用于使时间序列基础模型适应区域干旱预测的轻量级包装器
Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting
查看机构详情
- Adelaide University（阿德莱德大学）
- CSIRO（联邦科学与工业研究组织）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
研究针对时间序列基础模型应用于区域干旱预测的挑战，提出轻量级黑盒适应框架，通过SMR²和MBB两个包装器在推理时增强模型，在南澳大利亚站点预测中提升性能，降低MSE，实现资源受限区域的实际部署。
中文摘要 AI 辅助
大型时间序列基础模型（TSFMs）在不同领域展现出强大的零样本预测能力。但将其应用于区域气候预测面临实际挑战，传统微调方法不可行。为此，我们引入一个轻量级黑盒适应框架，通过两个即插即用包装器在推理时增强冻结的TSFMs。SMR²分解输入为多分辨率时间视图，学习特定步长的残差校正以捕捉区域动态，然后自适应集成预测；MBB通过块重采样保留时间依赖性并在时间连贯的残差扰动上进行集成以稳定点预测。在南澳大利亚多个站点提前一个月的标准化降水蒸散指数（SPEI）预测上进行评估，我们的框架持续提高了多个骨干模型的预测性能，在相应冻结骨干模型上平均平方误差（MSE）最多降低26%，并能在资源受限的区域预测系统中实际部署。
英文摘要
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 Residual), which decomposes the input into multi-resolution temporal views, learns stride specific residual corrections that capture regional dynamics, then adaptively ensembles them into a single forecast, and \textbf{MBB} (Moving Block Bootstrap), which preserves temporal dependencies through block resampling and ensembles over temporally coherent residual perturbations to stabilize the point forecast. Both wrappers instantiate the same bagging style principle: they build diverse views of the input or its residuals, forecast each with the same frozen backbone, and aggregate, so all adaptation comes from inference time ensembling rather than any weight update. Evaluated on one month ahead Standardized Precipitation Evapotranspiration Index (SPEI) prediction across multiple sites in South Australia, our framework consistently improves forecasting performance across several backbone models, demonstrating up to 26\% mean squared error (MSE) reduction over the corresponding frozen backbone while enabling practical deployment in resource constrained regional forecasting systems.
