RobynMeta
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TimestralMistral AI
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About
Robyn is an open source, experimental Marketing Mix Modeling (MMM) package developed by Meta’s Marketing Science team. It’s designed to help advertisers and analysts build rigorous, data-driven models that quantify how different marketing channels contribute to business outcomes (like sales, conversions, or other KPIs) in a privacy-safe, aggregated way. Rather than relying on user-level tracking, Robyn analyzes historical time-series data, combining marketing spend or reach data (ads, promotions, organic efforts, etc.) with outcome metrics, to estimate incremental impact, saturation effects, and carry-over (adstock) dynamics. Under the hood, Robyn blends classical statistical methods with modern machine learning and optimization; it uses ridge regression (to regularize against multicollinearity in many-channel models), time-series decomposition to isolate trend and seasonality, and a multi-objective evolutionary algorithm.
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About
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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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Not Supported
On-Premises
Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Marketing teams, data scientists, and analysts interested in a solution to measure real incremental impact, attribute performance across channels, and optimize budget allocation in a transparent, privacy-compliant way
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Audience
Data scientists, machine-learning engineers, forecasting practitioners, and teams developing time-series forecasting systems.
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Support
Phone Support
Not Supported
24/7 Live Support
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Online
Supported
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Support
Phone Support
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24/7 Live Support
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Online
Supported
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and VideosNo images available
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Pricing
Free
Free Version
Supported
Free Trial
Not Supported
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Pricing
No information available.
Free Version
Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
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Live Online
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In Person
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Training
Documentation
Supported
Webinars
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Live Online
Not Supported
In Person
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Company InformationMeta
Founded: 2004
United States
facebookexperimental.github.io/Robyn/
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Company InformationMistral AI
Founded: 2023
France
github.com/mistralai/timestral
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Categories |
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Integrations
Facebook
Supported
Facebook Ads
Supported
Instagram Ads
Supported
Meta Ads
Supported
Meta Pixel
Supported
Mistral Forge
Not Supported
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Integrations
Facebook
Not Supported
Facebook Ads
Not Supported
Instagram Ads
Not Supported
Meta Ads
Not Supported
Meta Pixel
Not Supported
Mistral Forge
Supported
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