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features
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support

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Description

Frogcast is a cutting-edge global weather forecasting API that provides extremely precise and localized weather predictions by integrating over 20 advanced numerical weather prediction models with state-of-the-art artificial intelligence and diverse geospatial data from top-tier organizations such as ECMWF and NOAA, alongside earth observation data and ground-level measurements, to aid crucial decision-making in fields like agriculture, construction, logistics, retail, insurance, environment, transport, and health. Its RESTful API grants access to an impressive array of more than 26 meteorological variables, offering forecasts that extend up to 15 days into the future, along with probabilistic forecasts that include confidence intervals and quantiles for enhanced understanding, all while delivering high spatial resolution down to 1 km and detailed topographic information at 90 m for focused targeting of specific areas. The Frogcast platform is designed for effortless integration into current systems and applications via intuitive API endpoints utilizing standard web protocols and JSON data formats, which facilitates both real-time and historical weather data analysis for trend identification and decision support. Additionally, the flexibility of the API allows for customization based on user needs, enhancing the overall user experience and effectiveness of the weather data provided.

Description

Timestral represents a suite of time-series foundation models along with a Python inference toolkit designed to create multivariate probabilistic forecasts based on historical data sequences. It comes in four different model sizes—5M, 28M, 123M, and 209M parameters—that are capable of handling past-only, known-future, and categorical covariates, providing quantile forecasts while accommodating context windows as extensive as 32,768 time steps. Users have the flexibility to load pretrained weights either from the Hugging Face Hub or directly from local files, enabling them to perform inference on both CPU and GPU. Such forecasts can be seamlessly integrated into various applications, including monitoring systems, capacity planning, energy management, supply chain logistics, financial risk assessment, or maintenance operations. Furthermore, Timestral offers zero-shot forecasting capabilities and can be tailored to specific domain data through the Mistral Forge. This package is primarily aimed at data scientists, machine learning engineers, and collaborative teams focused on developing robust forecasting solutions; the repository code is licensed under Apache 2.0, while the model weights are governed by a separate Modified MIT license. With its comprehensive features, Timestral is positioned to significantly enhance the accuracy and efficiency of predictive analytics in numerous industries.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

No images available

Integrations

Go REST Yes 
JSON Yes 
Mistral Forge No 

Integrations

Go REST No 
JSON No 
Mistral Forge Yes 

Pricing Details

€54 per 400 requests per month
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) Yes 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

QUOS Group

Founded

2021

Country

France

Website

www.frogcast.com

Vendor Details

Company Name

Mistral AI

Founded

2023

Country

France

Website

github.com/mistralai/timestral

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Product Features

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