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Description
Data Poem stands out as a leading Enterprise Decision AI firm, addressing the complexities faced by large organizations that often rely on numerous isolated models and dashboards that merely report past events without offering actionable insights. Its flagship product, POEM365, integrates these disparate systems into a cohesive causal framework, illustrating the relationships between various factors like spending, demand, and pricing as they influence revenue streams. With a robust foundation built on 250 billion transactions and an impressive five trillion dollars of spending data, the model can be implemented in approximately six weeks. Major brands within the Fortune 500 across sectors such as consumer packaged goods, retail, automotive, and e-commerce leverage this technology to enhance their revenue forecasts, budget planning, campaign optimization, and overall growth strategies. This transformative approach not only streamlines decision-making processes but also empowers organizations to adapt proactively to market changes.
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.
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Deployment
Web-Based
Yes
On-Premises
No
iPhone App
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iPad App
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Android App
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Windows
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Mac
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Linux
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Chromebook
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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
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Live Rep (24/7)
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Online Support
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Customer Support
Business Hours
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Live Rep (24/7)
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Online Support
Yes
Types of Training
Training Docs
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Live Training (Online)
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In Person
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Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Data Poem
Founded
2019
Country
United States
Website
datapoem.ai/
Vendor Details
Company Name
Founded
1998
Country
United States
Website
research.google/blog/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting/