TimesFM(시계열 기초 모델)은 사전 학습된 시계열 기초 도구입니다 구글 리서치에서 시계열 예측을 위해 개발한 모델입니다.

• 종이: 시계열 예측을 위한 디코더 전용 기초 모델, ICML 2024.
• 🆕 타임스FM 3.0 체크포인트: google/timesfm-3.0-pytorch. 체크포인트 2.5까지: 타임스FM 포옹 얼굴 컬렉션.
• 구글 리시치 블로그 게시물:
• TimesFM-3: 다변량 예측을 위한 제로 샷 기초 모델
• 시계열 예측을 위한 디코더 전용 기초 모델
• 구글 1P 제품의 TimesFM:
• 빅쿼리 ML: 확장성과 신뢰성을 위한 엔터프라이즈 수준의 SQL 쿼리.
• 구글 시트: 일일 스프레드시트를 위해서요.
• 정점 모델 정원: 에이전트 콜링을 위한 Docker화된 엔드포인트입니다.

이 오픈 버전은 공식적으로 지원되는 구글 제품이 아닙니다.

최신 모델 버전: TimesFM 3.0 ✨

아카이브된 모델 버전:

• 2.5: src/timesfm 관련 코드.
• 1.0 및 2.0: 관련 코드는 v1에 보관됨. ‘pip’ install timesfm==1.3.0’ 이 패키지의 이전 버전을 설치하여 로드하세요 그들.

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업데이트 — 2026년 9월

타임스FM 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.