Audience
Data scientists, researchers, and developers wanting to forecast multiple related time series and incorporate historical and known future signals
About TimesFM-3
TimesFM-3 is a state-of-the-art time series foundation model designed for highly accurate multivariate forecasting in a single forward pass. The 330 million parameter model is pre-trained on a real-world and synthetic time-series corpus comprising more than 1 trillion time points, building on the efficiency and zero-shot generalization of earlier TimesFM models. It can jointly predict multiple coevolving time series and capture dependencies that improve accuracy without task-specific fine-tuning. The model supports multiple targets with point and quantile forecasts, past covariates that are known only historically, and past-future dynamic covariates such as planned promotions, holidays, or weather forecasts. TimesFM-3 uses a decoder-only transformer architecture, processes contiguous data in patches of 32 time steps, and applies alternating causal temporal attention and full variate attention to combine patterns across time and related series.
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Integrations
No integrations listed.
Company Information
Google
Founded: 1998
United States
research.google/blog/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting/
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Product Details
Platforms Supported
Cloud
Training
Documentation
Videos
Support
Online
TimesFM-3 Frequently Asked Questions
Q: What kinds of users and organization types does TimesFM-3 work with?
TimesFM-3 works with these users and organization types: Mid Size Business, Small Business, Enterprise, Freelance, Nonprofit, Government, and Startup.
Q: What languages does TimesFM-3 support in their product?
TimesFM-3 supports these languages: English.
Q: What kind of support options does TimesFM-3 offer?
TimesFM-3 offers support via online.
Q: What type of training does TimesFM-3 provide?
TimesFM-3 provides training in the form of documentation and videos.
