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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 Data Poem?

Data Poem emerges as a prominent leader in the realm of Enterprise Decision AI, tackling the challenges that large corporations encounter when they depend on a multitude of disconnected models and dashboards that only reflect historical data without providing meaningful insights. Their premier offering, POEM365, seamlessly unifies these varied systems into a comprehensive causal framework that clarifies how different elements such as expenditure, demand, and pricing interplay to drive revenue. Built upon an extensive foundation of 250 billion transactions and a staggering five trillion dollars in spending information, this model can be operational within a mere six weeks. Numerous Fortune 500 companies across diverse industries, including consumer packaged goods, retail, automotive, and e-commerce, utilize this innovative technology to refine their revenue predictions, budget strategies, campaign enhancements, and growth initiatives. By adopting this groundbreaking methodology, organizations not only simplify their decision-making processes but also gain the capability to respond adeptly to shifts in the market landscape. Ultimately, this positions them for sustained success in an ever-evolving business environment.

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Integrations Supported

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Integrations Supported

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API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Not specified

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

Data Poem

Date Founded

2019

Company Location

United States

Company Website

datapoem.ai/

Categories and Features

AI Models

Not specified

Foundation Models

Not specified

Categories and Features

Decision Intelligence

Not specified

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