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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.

Description

WeatherNext represents a suite of AI-driven models developed by Google DeepMind and Google Research, designed to deliver cutting-edge weather predictions. These advanced models surpass conventional physics-based approaches in both speed and efficiency, leading to enhanced reliability in forecasts. By improving the accuracy of weather predictions, these innovations could significantly aid in disaster preparedness, ultimately saving lives during severe weather scenarios and bolstering the dependability of renewable energy sources and supply chains. WeatherNext Graph stands out by providing more precise and efficient deterministic forecasts than existing systems, producing a single forecast for each specified time and location with a 6-hour temporal resolution and a 10-day lead time. In addition, WeatherNext Gen excels at generating ensemble forecasts that outshine the current predominant models, thereby equipping decision-makers with a clearer understanding of weather uncertainties and the associated risks of extreme weather conditions. This leap in forecasting capability promises to transform how we respond to and manage the impacts of climate variability.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

AlphaCode No 
AlphaEvolve No 
AlphaFold No 
Chinchilla No 
Gemini No 
Gemini Deep Research No 
Gemini Diffusion No 
Gemini Enterprise No 
Gemini Enterprise Agent Platform No 
Gemini Robotics No 
GitHub No 
Google AI Studio No 
Google Cloud BigQuery No 
Google Cloud Platform No 
Google Earth Engine No 
Gopher No 
Music AI Sandbox No 
Project Mariner No 

Integrations

AlphaCode Yes 
AlphaEvolve Yes 
AlphaFold Yes 
Chinchilla Yes 
Gemini Yes 
Gemini Deep Research Yes 
Gemini Diffusion Yes 
Gemini Enterprise Yes 
Gemini Enterprise Agent Platform Yes 
Gemini Robotics Yes 
GitHub Yes 
Google AI Studio Yes 
Google Cloud BigQuery Yes 
Google Cloud Platform Yes 
Google Earth Engine Yes 
Gopher Yes 
Music AI Sandbox Yes 
Project Mariner Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

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) No 
Online Support Yes 

Customer Support

Business Hours Yes 
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 Yes 
Live Training (Online) No 
In Person Yes 

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

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

Vendor Details

Company Name

Google DeepMind

Founded

2010

Country

United Kingdom

Website

deepmind.google/science/weathernext/

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Product Features

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