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What is TimesFM-3?

TimesFM-3 is an innovative foundation model for time series analysis, distinguished by its capability to deliver highly accurate multivariate forecasts with a single forward pass. With a substantial framework of 330 million parameters, this model has been pre-trained on an extensive dataset comprising both real-world and synthetic time series information, accumulating over 1 trillion time points, which significantly bolsters its effectiveness and zero-shot generalization compared to its predecessors. It excels at simultaneously forecasting multiple coevolving time series while effectively recognizing dependencies that enhance predictive accuracy without necessitating task-specific fine-tuning. Additionally, it is designed to handle various forecasting objectives, including point and quantile predictions, and takes into account both historical covariates and dynamic covariates related to future scenarios, such as planned promotions, holidays, or shifts in weather. Employing a decoder-only transformer architecture, TimesFM-3 adeptly processes sequential data in chunks of 32 time steps, utilizing alternating causal temporal attention and full variate attention to seamlessly weave together patterns across time and interconnected series. As a result, this model serves as a powerful resource for forecasting intricate, time-dependent phenomena across diverse applications, making it a significant advancement in the field of time series forecasting. Its versatility and precision open up new avenues for exploration and application in various domains.

What is OPT?

Large language models, which often demand significant computational power and prolonged training periods, have shown remarkable abilities in performing zero- and few-shot learning tasks. The substantial resources required for their creation make it quite difficult for many researchers to replicate these models. Moreover, access to the limited number of models available through APIs is restricted, as users are unable to acquire the full model weights, which hinders academic research. To address these issues, we present Open Pre-trained Transformers (OPT), a series of decoder-only pre-trained transformers that vary in size from 125 million to 175 billion parameters, which we aim to share fully and responsibly with interested researchers. Our research reveals that OPT-175B achieves performance levels comparable to GPT-3, while consuming only one-seventh of the carbon emissions needed for GPT-3's training process. In addition to this, we plan to offer a comprehensive logbook detailing the infrastructural challenges we faced during the project, along with code to aid experimentation with all released models, ensuring that scholars have the necessary resources to further investigate this technology. This initiative not only democratizes access to advanced models but also encourages sustainable practices in the field of artificial intelligence.

Media

Media

No images available

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

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

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

www.meta.com

Categories and Features

Categories and Features

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