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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

TimesFM-3 represents an advanced time series foundation model that excels in highly precise multivariate forecasting with a single forward pass. This model, which consists of 330 million parameters, has undergone pre-training on a vast corpus of real-world and synthetic time series data, totaling over 1 trillion time points, thereby enhancing the effectiveness and zero-shot generalization capabilities seen in previous TimesFM iterations. It is adept at simultaneously predicting numerous coevolving time series and understanding dependencies that bolster accuracy without the need for task-specific fine-tuning. Furthermore, it accommodates multiple forecasting targets, including both point and quantile predictions, and incorporates past covariates that are only available historically, alongside dynamic covariates that pertain to future events such as planned promotions, holidays, or weather changes. Utilizing a decoder-only transformer architecture, TimesFM-3 processes sequential data in segments of 32 time steps, employing alternating causal temporal attention and full variate attention to integrate patterns across both time and interrelated series effectively. As a result, it provides a robust tool for forecasting complex time-dependent phenomena in various applications.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Go REST Yes 
JSON Yes 

Integrations

Go REST No 
JSON No 

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 No 

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 Yes 
On-Premises No 
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

Google

Founded

1998

Country

United States

Website

research.google/blog/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting/

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Product Features

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