Audience
Data scientists, machine-learning engineers, forecasting practitioners, and teams developing time-series forecasting systems.
About Timestral
Timestral is a family of time-series foundation models and a Python inference package for generating multivariate probabilistic forecasts from historical sequences. The four model sizes (5M, 28M, 123M and 209M parameters) support past-only, known-future and categorical covariates, return quantile forecasts, and accept context windows up to 32,768 time steps. Developers can load pretrained weights from the Hugging Face Hub or from local files and run inference on CPU or GPU, then incorporate forecasts into monitoring, capacity-planning, energy, supply-chain, financial-risk, or maintenance workflows. Timestral supports zero-shot forecasting and can be adapted to domain data through Mistral Forge. The package is intended for data scientists, machine-learning engineers, and teams building forecasting systems; repository code is Apache 2.0, while model weights use a separate Modified MIT license.
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Pricing
Free Version:
Free Version available.
Company Information
Mistral AI
Founded: 2023
France
github.com/mistralai/timestral
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Product Details
Platforms Supported
On-Premises
Training
Documentation
Support
Online
Timestral Frequently Asked Questions
Q: What kinds of users and organization types does Timestral work with?
Timestral works with these users and organization types: Mid Size Business, Small Business, Enterprise, Freelance, Nonprofit, Government, and Startup.
Q: What languages does Timestral support in their product?
Timestral supports these languages: English.
Q: What kind of support options does Timestral offer?
Timestral offers support via online.
Q: What other applications or services does Timestral integrate with?
Timestral integrates with: Mistral Forge.
Q: Does Timestral have an API?
Yes, Timestral has an API.
Q: What type of training does Timestral provide?
Timestral provides training in the form of documentation.
