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Description

NVIDIA Earth-2 is an advanced platform designed for high-performance, AI-enhanced weather and climate forecasting, utilizing a combination of open-source models, libraries, and frameworks to facilitate professional-grade predictions without the necessity for traditional supercomputing resources. This innovative system offers comprehensive capabilities, starting from the ingestion of raw observational data and culminating in the generation of high-resolution weather and climate forecasts, which include localized storm predictions and medium-range forecasts that extend up to 15 days. By leveraging generative AI architectures, Earth-2 significantly accelerates computational processes while either maintaining or enhancing accuracy when compared to more conventional forecasting techniques. The Earth-2 suite features a range of models such as Atlas, which specializes in multi-variable medium-range forecasting, StormScope for short-term local weather predictions, HealDA for global data assimilation, CorrDiff for regional resolution downscaling, and FourCastNet 3 for efficient global forecasting. Furthermore, this platform not only enhances predictive capabilities but also democratizes access to advanced forecasting technologies, making them more readily available to a wider audience.

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

API Access

Has API

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Integrations

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Integrations

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Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

NVIDIA

Founded

1993

Country

United States

Website

www.nvidia.com/en-us/high-performance-computing/earth-2/

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

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

Product Features

Product Features

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