零样本模型可直接预测叶面积指数，效果优于专用训练模型。
Zero-Shot Transfer Capabilities of the Sundial Foundation Model for Leaf Area Index Forecasting
- 用长时序输入激活基础模型，实现无需调参的预测
- 覆盖多个完整季节周期时，性能超越全监督LSTM
- 适合农业与环境监测中快速部署的遥感时序预测
本研究探讨时间序列基础模型在叶面积指数（LAI）预测中的零样本能力。基于美国2000-2022年HiQ数据集，系统比较了统计基线、全监督LSTM以及Sundial基础模型在多种评估协议下的表现。结果表明，在输入上下文窗口足够长的情况下——即覆盖超过一两个完整季节周期时，Sundial在零样本设置下可超越全训练的LSTM。这说明通用预训练时间序列模型无需任务特定微调即可在遥感时序预测中超越专用监督模型。这些发现凸显了预训练时间序列基础模型在农业与环境监测中作为即插即用预测器的巨大潜力。
原文摘要 · Abstract (English)
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.
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