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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

Google AI 推出了 TimesFM-3,这是一款专为多元时间序列预测设计的新型基础模型。与仅限于单变量预测的前代模型不同,TimesFM-3 可以同时预测多个相关时间序列,并整合历史和已知的未来外部因素。该模型拥有 3.3 亿个参数,在超过万亿个时间点上进行了训练,并采用了一种新颖的架构,具有因果时间注意力(causal temporal attention)和全变量注意力(full variate attention),以捕捉复杂的序列间依赖关系,并在单次前向传播中生成预测。 AI

影响 通过实现多个相关时间序列的同时预测以及整合未来外部数据,增强了预测能力。

排序理由 前沿实验室模型发布,附带系统卡。[lever_c_demoted from frontier_release: ic=1 ai=1.0]

在 Google AI / Research 阅读 →

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Google AI 发布 TimesFM-3 用于多元时间序列预测

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前沿实验室模型发布,附带系统卡。[lever_c_demoted from frontier_release: ic=1 ai=1.0]
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报道来源 [1]

  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