Timestral
作者: Adrien Petralia, Alexandre Gavaudan, Artjom Joosen, Etienne Goffinet, Richard Kurle, Yassine El Ouahidi, Yassir Bendou, Abdelaziz Bounhar, Abhijeet Somani, Aditi Kabra, Adrian Valente, Adrien Sadé, Ahmed Djermani, Alan Jeffares, Albert Jiang, Alberto Bietti, Aleksandr Timashov, Alexandre Cahill, Alexandre Laval, Alexandre Sablayrolles, Alexandros Sanchez Vassopoulos, Amélie Héliou, Amos You, André Jonasson, Andreas Fürst, Andrew Bai, Andrew Ehrenberg, Andrew Zhao, Andy Lo, Angele Lenglemetz, Anmol Agarwal, Antonia Calvi, Arata Suzuki, Arjun Majumdar, Arthur Fournier, Ashwani Kumar, Augustin Lombard, Avinash Sooriyarachchi, Aylin Guliz Akkus, Aysenur Karaduman, Baptiste Bout, Baptiste Rozière, Barna Pásztor, Baudouin De Monicault, Benjamin Holzschuh, Benjamin Tibi, Benno Stein, Bernhard Stadlbauer, Bhargavi Bhaskar, Binbing Hou, Błażej Osiński, Boris van Breugel, Camille Le Scao, Chaoran Yu, Charlotte Cronjäger, Chen-Yo Sun, Chris Bamford, Christian Wallenwein, Christophe Renaudin, Clémence Lanfranchi, Colin Bonatti, Corentin Barreau, Corentin Sautier, Cristiana-Diana Diaconu, Cyprien Courtot, Daniel Lopes, Daniel Marczak, Darius Dabert, Diego de las Casas, Dominik Nuss, Dylan Rubini, Dzmitry Soupel, Elizaveta Demyanenko, Elliot Chane-Sane, Emilien Fugier, Emmanuel Gottlob, Erik Aas, Etienne Duchesne, Fabian Paischer, Fabian Schlager, Faruk Ahmed, Federico Baldassarre, Félix Rosseeuw, Filip Szatkowski, Filippo Vicentini, Florian Wiesner, Gabrielle Berrada, Gaëtan Ecrepont, Gaspard Blanchet, Gaspard Donada-Vidal, Gaurav Mahajan, Gauthier Guinet, Genevieve Hayes, Georgii Novikov, Georgios Christoglou, Giada Pistilli, Gonzague de Carpentier, Graeme Nail, Guillaume Breton, Guillaume Kunsch, Guillaume Lample, Guillaume Martin, Guillaume Raille, Guillaume Richard, Guillem Simeon, Gunjan Dhanuka, Gunshi Gupta, Hamza Senhaji Rhazi, Han Zhou, Hédi Hadiji, Himanshu Jaju, Hope McGovern, Hubert de Parseval, Hugo Cisneros, Hugo Germain, Hugo Thimonier, Hugo Venturini, Hyeonju Lee, Hyunwoo Oh, Iliass Lasri, Ilyas Oulkadda, Jacques Sun, Jan Ludziejewski, Jason Rute, Jean de Becdelièvre, Jean Quentin, Jean-Malo Delignon, Jessica Chudnovsky, Jianchao Li, Jie Zhang, Jin Bin Ou, Joachim Studnia, Joep Barmentlo, Johannes Brandstetter, John Harvill, Jonas Amar, Jonas Schweizer, Jonathan Roberts, Joséphine Delas, Josselin Somerville, Jules Depersin, Julien Denize, Julien Tauran, Julien Verlaguet, Kartik Khandelwal, Khyathi Raghavi Chandu, Kilian Tep, Konstantin Mishchenko, Kris Szczepaniak, Kush Jain, Larissa Laich, Laura Calem, Laurence Aitchison, Laurent Callot, Laurent Fainsin, Léo Cotteleer, Léonard Blier, Loïc Chadoutaud, Loïck Chambon, Lorenzo Proietti, Louis Faucon, Louis Martin, Louis Serrano, Lu Li, Luc Boudier, Lucas Fernandes Martins, Lucas Mebille, Lucile Saulnier, Ludovic Ho Fuh, Luis Montero, Łukasz Borchmann, Maarten Buyl, Malik Hassan Raza, Manon Chossegros, Marcin Możejko, Margaret Jennings, Markus Hennerbichler, Martin Alexandre, Mathieu Poirée, Mathieu Schmitt, Mathilde Guillaumin, Mathis Felardos, Matthieu André, Matthieu Dinot, Matthieu Futeral, Maurits Bleeker, Mauro Comi, Max Mynter, Maxim Berman, Maxime Darrin, Maxime Langelier, Maxime Louis, Maximilian Augustin, Maximilian Müller, Mehdi Bouaziz, Melina Jingting Laimon, Mert Unsal, Mia Chiquier, Michael Pilcer, Michał Pietruszka, Michał Zając, Mickaël Seznec, Mikhail Biriuchinskii, Minh-Quang Pham, Minwoo Kang, Mircea Lica, Morgane Rivière, Namit Katariya, Natasha Ørregaard Klingenbrunn, Nathan Bout, Nathan Grinsztajn, Nathan Simpson, Neeraj Aggarwal, Neil Ashton, Neil Mallinar, Nick Bührer, Nicolas Moreau, Nicolò Lucchesi, Ola Mysiak, Oliver Leicht, Olivier Bousquet, Olivier Koch, Parag Jain, Patricia Wang, Patrick Blies, Patrick von Platen, Patryk Saffer, Paul Jacob, Paul Teiletche, Paul Wambergue, Paula Kurylowicz, Pavan Kumar Reddy, Pavel Kuksa, Philippe Formont, Philippe Pinel, Philomène Chagniot, Pierre Glandon, Pierre Stock, Pierre-André Savalle, Piotr Miłoś, Poojan Shah, Prateek Gupta, Pravesh Agrawal, Pui Ki Chan, Quentin Desreumaux, Quentin Selle, Quentin Torroba, Quentin Wargnier, Quercus Hernandez, Ram Ramrakhya, Randall Isenhour, Ranjit Parva, Raphaël Balouka, Raphaël Rialland, Raul Perez Pelaez, Reinhard Sonnleitner, Remi Delacourt, Rishi Shah, Rob Romijnders, Rohan Shah, Rohin Arora, Romain Sauvestre, Roman Soletskyi, Ronan Riochet, Rosalie Millner, Rupert Menneer, Sagar Vaze, Samuel Barry, Samuel Belkadi, Samuel Humeau, Samuele Papa, Sanchit Gandhi, Sandeep Cranganore, Sandeep Subramanian, Sarthak Mittal, Saskia Adaime, Scott Matthews, Sean Cha, Sebastian Kaltenbach, Seungjae Jung, Shashwat Verma, Sherif Waly, Shrimai Prabhumoye, Siddhant Waghjale, Siddharth Gandhi, Simon Lepage, Simon Sorg, Soham Ghosh, Sophie Marbach, Srijan Mishra, Stanislas Lange, Steve Hong, Sumukh Aithal, Szymon Antoniak, Tarun Kumar Vangani, Teddy Turquet, Teven Le Scao, Théo Cachet, Thibaut Lavril, Thomas Chabal, Thomas Coste, Thomas Defard, Thomas Foubert, Thomas Loux, Thomas Robert, Thomas Wang, Tianyu Zhang, Tim Lawson, Timothée Lacroix, Timothy Wallis, Tobias Kronlachner, Tom Bewley, Tom Edwards, Tomas Hodan, Triantafyllos Afouras, Tuhin Das, Tyler Wang, Udit Saxena, Ulrick Blé, Umar Jamil, Umberto Tomasini, Valentin Macé, Van Phung, Venkata Reddy, Vibha Masti, Victor Jouault, Victor Letzelter, Victor Paltz, Victor Poucheret, Vincent Maladière, Vincent Pfister, Virgile Richard, Vladislav Bataev, Wassim Bouaziz, Wen Ding Li, William Flanagan, William Havard, William Marshall, Witold Jędrzejewski, Xinghui Li, Xingran Guo, Xinyu Yang, Xuanyu Zhang, Yann Dreze, Yann Viegas, Yannic Neuhaus, Yassine Turki, Yessin Moakher, Yi He, Yihan Wang, Yimu Pan, Yuying Zhu, Yves Martin des Taillades, Zaccharie Ramzi, Zhenlin Xu, Zsófia Csákány
分类: cs.AI
发布日期: 2026-10-27
备注: Webpage: https://mistral.ai/news/timestral ; Weights: https://huggingface.co/collections/mistralai/timestral ; Code: https://github.com/mistralai/timestral
💡 一句话要点
提出Timestral以解决时间序列预测问题
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 时间序列预测 解耦预测视角 因果架构 模型预训练 基准测试
📋 核心要点
- 现有时间序列预测方法在长时间预测和上下文利用方面存在局限性，难以有效处理复杂数据。
- 论文提出的解耦预测视角（DHF）方法，通过分离预测标记与上下文，提升了长时间预测的准确性。
- 实验结果显示，Timestral在多个基准测试中表现优异，尤其是Timestral-209M和Timestral-123M在所有指标上均名列前茅。
📝 摘要（中文）
我们介绍了Timestral，一个包含四个时间序列基础模型的系列，分别具有5M、28M、123M和209M参数。Timestral在GIFT-Eval、TIME和fev-bench上设定了新的最先进水平，其中Timestral-209M和Timestral-123M在每个指标上均名列前茅。我们通过三个关键要素实现了这一点：解耦的预测视角（DHF），将预测标记与因果编码上下文分离；支持已知未来协变量的因果架构；以及一个两阶段的训练流程，先在多样化的大规模合成语料上进行预训练，再在真实和合成数据的混合上进行后训练。本文描述了模型设计及其基准结果。所有四个模型的权重可在Hugging Face上获取，推理代码可在GitHub上找到。
🔬 方法详解
问题定义：本论文旨在解决时间序列预测中的长时间预测问题，现有方法在处理复杂上下文时常常面临性能瓶颈，尤其是在长时间预测的准确性上存在不足。
核心思路：论文提出的解耦预测视角（DHF）通过将预测标记与因果编码上下文分离，使得模型能够在不遮蔽上下文的情况下进行长时间预测，从而提高预测的准确性和可靠性。
技术框架：整体架构包括三个主要阶段：首先在多样化的大规模合成语料上进行预训练；然后在真实和合成数据的混合上进行后训练；最后，模型通过支持已知未来协变量的因果架构来增强预测能力。
关键创新：最重要的技术创新在于解耦的预测视角（DHF），它允许模型在进行长时间预测时不需要遮蔽上下文信息，这与传统方法的设计思路有本质区别。
关键设计：模型的参数设置包括多个不同规模的版本（5M、28M、123M、209M），并采用了适应性损失函数和优化算法，以确保在不同数据集上的最佳性能。
🖼️ 关键图片
📊 实验亮点
实验结果表明，Timestral在GIFT-Eval、TIME和fev-bench等多个基准测试中设定了新的最先进水平，特别是Timestral-209M和Timestral-123M在所有指标上均名列前茅，显示出显著的性能提升，超越了现有的主流模型。
🎯 应用场景
Timestral的研究成果在多个领域具有广泛的应用潜力，包括金融市场预测、气象预报、供应链管理等。其高效的时间序列预测能力能够帮助企业和机构更好地进行决策，优化资源配置，提升运营效率。未来，随着模型的进一步优化和应用场景的扩展，Timestral有望在更多实际问题中发挥重要作用。
📄 摘要（原文）
We introduce Timestral, a family of four time series foundation models for probabilistic forecasting, with 5M, 28M, 123M, and 209M parameters. Timestral sets a new state of the art on GIFT-Eval, TIME, and fev-bench, where Timestral-209M and Timestral-123M take the top two places on every metric. We achieve this with three ingredients: Decoupled Horizon Forecasting (DHF), which separates prediction tokens from the causally encoded context, so that longer horizons are trained without masking context and each observed value is both a forecast target and context for later forecasts; a covariate framework that natively supports known-future covariates within a causal architecture; and a two-stage pipeline that pretrains exclusively on a diverse, large-scale synthetic corpus before post-training on a mix of real and synthetic data with extended context. This report describes the model design and its benchmark results. Weights for all four models are available on Hugging Face and inference code on GitHub.