Zero-Shot Transfer Capabilities of the Sundial Foundation Model for Leaf Area Index Forecasting
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909991856766976 |
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| author | Zhang, Peining Qin, Hongchen Zhang, Haochen Guo, Ziqi Wang, Guiling Bi, Jinbo |
| author_facet | Zhang, Peining Qin, Hongchen Zhang, Haochen Guo, Ziqi Wang, Guiling Bi, Jinbo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20004 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Zero-Shot Transfer Capabilities of the Sundial Foundation Model for Leaf Area Index Forecasting Zhang, Peining Qin, Hongchen Zhang, Haochen Guo, Ziqi Wang, Guiling Bi, Jinbo Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Zero-Shot Transfer Capabilities of the Sundial Foundation Model for Leaf Area Index Forecasting |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.20004 |