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Average Ratings 0 Ratings
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
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
Founded
1998
Country
United States
Website
research.google/blog/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting/