Robyn

Robyn

Meta
Timestral

Timestral

Mistral AI
+
+

Related Products

  • RaimaDB
    12 Ratings
    Visit Website
  • TrafficGuard
    89 Ratings
    Visit Website
  • CTM
    939 Ratings
    Visit Website
  • IUX
    968 Ratings
    Visit Website
  • 800.com
    2,436 Ratings
    Visit Website
  • MikMak
    85 Ratings
    Visit Website
  • Evertune
    1 Rating
    Visit Website
  • Superfiliate
    49 Ratings
    Visit Website
  • Vantage
    15,963 Ratings
    Visit Website
  • Yeastar P-Series PBX System
    116 Ratings
    Visit Website

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.

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.

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

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

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

Audience

Data scientists, machine-learning engineers, forecasting practitioners, and teams developing time-series forecasting systems.

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Not Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

No images available

Pricing

Free
Free Version Supported
Free Trial Not Supported

Pricing

No information available.
Free Version Supported
Free Trial Not Supported

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

Meta
Founded: 2004
United States
facebookexperimental.github.io/Robyn/

Company Information

Mistral AI
Founded: 2023
France
github.com/mistralai/timestral

Alternatives

Alternatives

TimesFM-3

TimesFM-3

Google
Solar Mini

Solar Mini

Upstage AI
Mistral NeMo

Mistral NeMo

Mistral AI

Categories

Categories

AI Models Supported

Integrations

Facebook Supported
Facebook Ads Supported
Instagram Ads Supported
Meta Ads Supported
Meta Pixel Supported
Mistral Forge Not Supported

Integrations

Facebook Not Supported
Facebook Ads Not Supported
Instagram Ads Not Supported
Meta Ads Not Supported
Meta Pixel Not Supported
Mistral Forge Supported
Claim Robyn and update features and information
Claim Robyn and update features and information
Claim Timestral and update features and information
Claim Timestral and update features and information