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English(EN) TimesFM-3: A zero-shot foundation model for multivariate forecasting

Google AI 发布 TimesFM-3 用于多元时间序列预测

作者 PulseAugur 编辑部 · [3 个来源] ·

Google AI 推出了 TimesFM-3，这是一个拥有 3.3 亿参数的基础模型，专为多元时间序列预测而设计。与仅限于单变量预测的前代模型不同，TimesFM-3 能够联合预测多个相关时间序列，并在无需任务特定微调的情况下纳入过去和未来的外部因素。该模型通过采用一种新颖的架构，结合了交替因果时间注意力和全变量注意力机制，在 GIFT-Eval 和 TIME 排行榜等多个基准测试中取得了最先进的性能。虽然代码可在 Apache-2.0 许可下使用，但模型权重仅限于非商业、非生产用途。 AI

影响 增强了多元时间序列预测能力，有望提高零售、金融和医疗保健等领域的准确性。

排序理由 来自主要 AI 实验室的研究论文和模型发布。

在 Google AI / Research 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

Google AI 发布 TimesFM-3 用于多元时间序列预测

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报道来源 [3]

  1. Google AI / Research TIER_1 English(EN) ·

    TimesFM-3：一种用于多元预测的零样本基础模型

    Data Management

  2. MarkTechPost TIER_1 English(EN) · Michal Sutter ·

    Google AI 发布 TimesFM-3：一个用于多元时间序列预测的 3.3 亿参数的零样本基础模型

    <p>Google Research has released TimesFM-3, a 330 million parameter time series foundation model that forecasts multiple related series in a single forward pass. Unlike every TimesFM checkpoint through 2.5, it is pretrained natively for multivariate forecasting, accepting multiple…

  3. r/singularity TIER_2 English(EN) · /u/Recoil42 ·

    TimesFM-3：一种用于多元预测的零样本基础模型

    <table> <tr><td> <a href="https://www.reddit.com/r/singularity/comments/1w3wyi8/timesfm3_a_zeroshot_foundation_model_for/"> <img alt="TimesFM-3: A zero-shot foundation model for multivariate forecasting" src="https://external-preview.redd.it/Ey2j2F814hde7hW3lIGeiFPclAXwvjBqAJpdW_…

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