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Description

Transform your warehouse operations and adapt to scalability demands through cutting-edge robotic automation developed by the foremost AI researchers globally. The Covariant Brain, trained on data from millions of picks across diverse warehouses, allows robots to efficiently and independently select nearly any SKU or product right from the start. Covariant’s AI-driven Robotic Putwall takes this a step further by autonomously organizing items from mixed-SKU containers, effectively bridging labor shortages while enhancing overall throughput. Each implementation is carefully customized to integrate seamlessly with your current floor layout, systems, and both upstream and downstream workflows. Additionally, with fleet learning capabilities, each robot benefits from the collective experience gained through millions of selections made by interconnected robots in warehouses worldwide. This means that as soon as they are deployed, Covariant robots are ready to tackle virtually any SKU or item, irrespective of its shape, size, or packaging. The impact of this technology not only streamlines operations but also significantly boosts efficiency and productivity in your warehouse environment.

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 No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

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 No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) Yes 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Covariant

Founded

2017

Country

United States

Website

covariant.ai/

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

Artificial Intelligence

Chatbot No 
For Healthcare No 
For Sales No 
For eCommerce No 
Image Recognition No 
Machine Learning No 
Multi-Language No 
Natural Language Processing No 
Predictive Analytics No 
Process/Workflow Automation No 
Rules-Based Automation No 
Virtual Personal Assistant (VPA) No 

Warehouse Management

3PL Management No 
Barcoding / RFID No 
Category Customization No 
Channel Management No 
Demand Planning No 
Inventory Management No 
Location Control No 
Order Management No 
Purchasing No 
Quality Control No 
Receiving / Putaway Management No 
Returns Management No 
Shipping Management No 

Product Features

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