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Zero-Shot Forecasting of In-Situ Soil Moisture and Temperature with a Time-Series Foundation Model — arXiv:2610.30971

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.30971
License (as archived): CC0-1.0
Archived: 2026-10-25T21:34:42.414Z
SHA-256 of the archived text: 01a0e0439d1f070a49edc95219dc6bba0bb19e41c1d0f7617f9f1821b6cbcb91
Stable URL of this page: https://metraipolis.com/documento/dc_cb6f00a8479a

How to cite

Zero-Shot Forecasting of In-Situ Soil Moisture and Temperature with a Time-Series Foundation Model — arXiv:2610.30971 — arXiv (en), https://arxiv.org/abs/2610.30971 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cb6f00a8479a (Metraipolis archive dc_cb6f00a8479a, sha256 01a0e0439d1f070a49edc95219dc6bba0bb19e41c1d0f7617f9f1821b6cbcb91)

BibTeX

@misc{dccb6f00a8479a,
  title = {Zero-Shot Forecasting of In-Situ Soil Moisture and Temperature with a Time-Series Foundation Model — arXiv:2610.30971},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.30971},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cb6f00a8479a; sha256 01a0e0439d1f070a49edc95219dc6bba0bb19e41c1d0f7617f9f1821b6cbcb91},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.30971. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Soil moisture and temperature govern plant-water availability and microbial activity, making them key indicators for evaluating land-restoration efforts on degraded landscapes. However, the physical and data-driven models normally used to forecast them both need information that a newly instrumented restoration site does not yet have: hydraulic parameters for the former and months of per-site training data for the latter. We evaluate TimesFM 1, a 500M-parameter time-series foundation model, zero-shot on 15-minute telemetry from 27 sensor stations across three land-management regimes (Control, Treatment, Scald) at a semi-arid restoration site, forecasting 5 cm and 30 cm soil moisture and enclosure temperature at seven lead times from 1 h to 168 h. Using gap-aware rolling-origin evaluation across sensors, we find that TimesFM beats the best rule-based statistical forecasting baselines (persistence, seasonal-naive, or 7-day climatology) by 21-76% at sub-daily lead times but loses by 6-79% beyond a day, a pattern explained by a diurnal-alignment mechanism that holds for both soil-moisture depths and temperature. A CatBoost baseline trained walk-forward on the same origins with full history and weather covariates never wins overall, and we show that its weakness is partly a cold-start effect specific to short deployments. We additionally quantify the inference cost of zero-shot forecasting on commodity CPU hardware. Our results highlight a practical trade-off between zero-shot forecasting capability, predictive performance, and computational efficiency.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.30971 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-25T21:25:18.126Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).