TimesFM（时间序列基础模型）是一种预训练的时间序列基础 由谷歌研究院开发用于时间序列预测的模型。

• 论文： 仅解码器的基础模型用于时间序列预测， ICML 2024。
• 🆕 TimesFM 3.0检查点： google/timesfm-3.0-pytorch。检查点最多2.5： TimesFM拥抱面孔系列。
• 谷歌研究博客文章：
• TimesFM-3：用于多变量预测的零样本基础模型
• 仅解码器的基础模型用于时间序列预测
• Google 1P产品中的TimesFM：
• BigQuery 机器学习： 企业级SQL查询以提升可扩展性和可靠性。
• 谷歌表格： 用于你的每日电子表格。
• 顶点模型花园： Docker化端点用于代理调用。

这个开放版本并非官方支持的谷歌产品。

最新型号版本： TimesFM 3.0 ✨

存档型号版本：

• 2.5：相关代码，属于src/timesfm。
• 1.0 和 2.0：相关代码存档于子目录 v1。你可以 ‘pip 安装 Timesfm==1.3.0’ 以安装该包的旧版本以加载 他们。

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更新 — 2026年9月

TimesFM 3。

TimesFM

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.

• Paper: A decoder-only foundation model for time-series forecasting, ICML 2024.
• 🆕 TimesFM 3.0 Checkpoint: google/timesfm-3.0-pytorch. Checkpoints up to 2.5: TimesFM Hugging Face Collection.
• Google Reseach blog posts:
  • TimesFM-3: A zero-shot foundation model for multivariate forecasting
  • A decoder-only foundation model for time-series forecasting
• TimesFM in Google 1P Products:
  • BigQuery ML: Enterprise level SQL queries for scalability and reliability.
  • Google Sheets: For your daily spreadsheet.
  • Vertex Model Garden: Dockerized endpoint for agentic calling.

This open version is not an officially supported Google product.

Latest Model Version: TimesFM 3.0 ✨

Archived Model Versions:

• 2.5: relevant code under src/timesfm.
• 1.0 and 2.0: relevant code archived in the subdirectory v1. You can pip install timesfm==1.3.0 to install an older version of this package to load them.

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Update — September 2026

TimesFM 3.0 finishes rollout in Google Cloud BigQuery ML, supporting commercial and production uses (see clarified license notice).

Update — August 2026

TimesFM 3.0 is out!

TimesFM 3.0 introduces native multivariate time-series forecasting, flexible covariate support (both past-only and past-and-future covariates), superior zero-shot generalist capabilities, and top performance across all three major time-series foundation model benchmarks.

Key Highlights:

• Native Multivariate & Univariate Forecasting with Covariates: Seamlessly forecast multi-channel multivariate series as well as individual univariate series, with native support for past-only and past-and-future dynamic covariates without per-task tuning.
• Top Benchmark Performance:
  • 🥇 fev-bench: Rank #1 overall across 100 diverse real-world forecasting tasks.