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MoTiF: a self-supervised model for multi-source forecasting with application to tropical cyclones – CORRIGENDUM

Published online by Cambridge University Press:  19 February 2026

Clément Dauvilliers Open the ORCID record for Clément Dauvilliers [Opens in a new window]  and
Claire Monteleoni

Abstract

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Information

Type
Corrigendum
Information
Environmental Data Science , Volume 5 , 2026 , e5
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2026. Published by Cambridge University Press

The authors regret that there is an error in section 3.1 of this article. When describing the Train-validation-test split, it is stated that, “The list of samples is divided into three splits: training, validation, and test, with proportions of 0.8, 0.15, and 0.15, respectively.” The true values are, “proportions of 0.7, 0.15, and 0.15 respectively.”

References

Dauvilliers, C and Monteleoni, C (2025) MoTiF: A self-supervised model for multi-source forecasting with application to tropical cyclones. Environmental Data Science. 4, e36. https://doi.org/10.1017/eds.2025.10014.CrossRefGoogle Scholar