TimesFM-3

TimesFM-3

Google
+
+

Related Products

  • Omnilert
    26 Ratings
    Visit Website
  • OptiSigns
    8,462 Ratings
    Visit Website
  • Dronedesk
    371 Ratings
    Visit Website
  • Pylon
    33 Ratings
    Visit Website
  • D&B Connect
    189 Ratings
    Visit Website
  • Qloo
    23 Ratings
    Visit Website
  • SKU Science
    16 Ratings
    Visit Website
  • Intellimas
    30 Ratings
    Visit Website
  • Martus
    187 Ratings
    Visit Website
  • Bridgit
    80 Ratings
    Visit Website

About

Meteomatics specializes in high-resolution commercial weather forecasting, power output forecasting for wind, solar and hydro, weather data gathering from the lower atmosphere using Meteodrones, and weather data delivery via the Weather API. - Unlimited accesses/day - Weather data querying via URL - Unified weather data access for historical and current weather, forecasts, climate models, and data from over 25 weather models - WMS and WFS interface - Delivery of forecasts with an average response time of 20 to 30 ms - 90 m downscaling worldwide - 1800+ parameters - Historical weather data from 1979 Climate data including climate scenarios up to the year 2100 - Secured use with HTTP and HTTPS - Integration with many formats, connectors, and programming languages available - Proprietary European Weather Model with 1 km resolution – EURO1k (Business plan)

About

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.

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 Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

Energy, Aviation, Shipping, Insurance, Agriculture, Transport, Logistics, Government, Defense

Audience

Data scientists, researchers, and developers wanting to forecast multiple related time series and incorporate historical and known future signals

Support

Phone Support 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 Supported

API

Offers API Not Supported

Screenshots and Videos

Screenshots and Videos

Pricing

$0/month/user
Prices for Business packages are calculated based on configuration. Please contact our sales managers to receive an offer.
Free Version Not Supported
Free Trial Not Supported

Pricing

No information available.
Free Version Not 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 Supported
Live Online Not Supported
In Person Supported

Training

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

Company Information

Meteomatics
Founded: 2012
Switzerland
meteomatics.com

Company Information

Google
Founded: 1998
United States
research.google/blog/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting/

Alternatives

Xweather

Xweather

Vaisala

Alternatives

CodeQwen

CodeQwen

Alibaba
Kimi K2

Kimi K2

Moonshot AI
Qwen-7B

Qwen-7B

Alibaba

Categories

Categories

AI Models Supported
Foundation Models Supported

Integrations

ArcGIS Supported
Google Maps Supported
Java Supported
JavaScript Supported
Julia Supported
Kotlin Supported
MATLAB Supported
Microsoft Power BI Supported
PHP Supported
Postman Supported
Python Supported
R Supported
Rust Supported
Swift Supported
Tableau Supported

Integrations

ArcGIS Not Supported
Google Maps Not Supported
Java Not Supported
JavaScript Not Supported
Julia Not Supported
Kotlin Not Supported
MATLAB Not Supported
Microsoft Power BI Not Supported
PHP Not Supported
Postman Not Supported
Python Not Supported
R Not Supported
Rust Not Supported
Swift Not Supported
Tableau Not Supported
Claim Meteomatics and update features and information
Claim Meteomatics and update features and information
Claim TimesFM-3 and update features and information
Claim TimesFM-3 and update features and information