TimesFM-3

TimesFM-3

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About

Address the change and scale of your warehouse operations with robotic automation, built by the world’s leading AI research scientists. Trained on millions of picks from Covariant robots in warehouses around the world, the Covariant Brain enables robots to autonomously pick virtually any SKU or item on Day One. Covariant’s AI-powered Robotic Putwall autonomously sorts items from mixed-SKU totes, closing labor gaps while increasing throughput. Each deployment is meticulously tailored to fit into your existing floorplan, systems, and upstream/downstream processes. With fleet learning, each robot learns from millions of picks performed by connected robots in warehouses around the world. Right out of the box, Covariant robots can handle virtually any SKU or item, regardless of shape, size, or packaging.

About

TimesFM-3 is a state-of-the-art time series foundation model designed for highly accurate multivariate forecasting in a single forward pass. The 330 million parameter model is pre-trained on a real-world and synthetic time-series corpus comprising more than 1 trillion time points, building on the efficiency and zero-shot generalization of earlier TimesFM models. It can jointly predict multiple coevolving time series and capture dependencies that improve accuracy without task-specific fine-tuning. The model supports multiple targets with point and quantile forecasts, past covariates that are known only historically, and past-future dynamic covariates such as planned promotions, holidays, or weather forecasts. TimesFM-3 uses a decoder-only transformer architecture, processes contiguous data in patches of 32 time steps, and applies alternating causal temporal attention and full variate attention to combine patterns across time and related series.

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

Organizations looking for an universal AI solution to give robots the ability to see, reason and act on the world around them

Audience

Data scientists, researchers, and developers wanting to forecast multiple related time series and incorporate historical and known future signals

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Not Supported

API

Offers API Not Supported

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation Not Supported
Webinars Not Supported
Live Online Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

Covariant
Founded: 2017
United States
covariant.ai/

Company Information

Google
Founded: 1998
United States
research.google/blog/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting/

Alternatives

covari

covari

Covari Technologies, Inc.

Alternatives

Timestral

Timestral

Mistral AI
TimesFM-3

TimesFM-3

Google
LocusONE

LocusONE

Locus Robotics
CodeQwen

CodeQwen

Alibaba
Kimi K2

Kimi K2

Moonshot AI
Qwen-7B

Qwen-7B

Alibaba

Categories

Categories

AI Models Supported
Foundation Models Supported

Integrations

No info available.

Integrations

No info available.
Claim Covariant and update features and information
Claim Covariant and update features and information
Claim TimesFM-3 and update features and information
Claim TimesFM-3 and update features and information