Sundial基础模型在叶面积指数预测中的零样本转移能力
Zero-Shot Transfer Capabilities of the Sundial Foundation Model for Leaf Area Index Forecasting
AI总结：
Sundial基础模型在无需任务特定调优的情况下，能够超越专门监督模型，在遥感时间序列预测中展现强大零样本转移能力。
摘要：
本文研究了时间序列基础模型在农业监测中叶面积指数（LAI）预测中的零样本预测能力。使用HiQ数据集（美国，2000-2022），我们系统比较了统计基线、全监督LSTM和Sundial基础模型在多种评估协议下的表现。我们发现，在零样本设置中，Sundial能够在输入上下文窗口足够长的情况下超越全训练的LSTM——具体来说，当覆盖超过一个或两个完整季节周期时。我们展示了通用基础模型可以在无需任务特定调优的情况下，在遥感时间序列预测中超越专门的监督模型。这些结果突显了预训练时间序列基础模型在农业和环境应用中作为有效即插即用预测器的强大潜力。
英文摘要：
This work investigates the zero-shot forecasting capability of time series foundation models for Leaf Area Index (LAI) forecasting in agricultural monitoring. Using the HiQ dataset (U.S., 2000-2022), we systematically compare statistical baselines, a fully supervised LSTM, and the Sundial foundation model under multiple evaluation protocols. We find that Sundial, in the zero-shot setting, can outperform a fully trained LSTM provided that the input context window is sufficiently long-specifically, when covering more than one or two full seasonal cycles. We show that a general-purpose foundation model can surpass specialized supervised models on remote-sensing time series prediction without any task-specific tuning. These results highlight the strong potential of pretrained time series foundation models to serve as effective plug-and-play forecasters in agricultural and environmental applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
