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Description
We enable leaders to swiftly make impactful choices through recommendations rooted in data analysis. Our innovative approach incorporates unique time series features and automated model selection, which serve as the core of our strategy to enhance forecasting precision. Rather than taking days, you can optimize various scenarios in just minutes, allowing for thorough discussions on implications and trade-offs that lead to sound recommendations. We also integrate external data sources such as commodity prices, social media trends, and significant events. Our collection of cloud-based applications is specifically designed for supply chain and financial operations. With a cutting-edge demand and consensus planning solution, we provide exceptional forecasting accuracy while prioritizing a customer-oriented workflow. In addition, our efficient cash management tool enables accurate forecasting and reveals crucial insights for effective cash savings. Furthermore, our machine learning-driven optimization application enhances production and multi-echelon inventory management, facilitating seamless control over the movement of goods and services while supporting better operational decisions. Together, these tools create a comprehensive ecosystem that empowers organizations to thrive in a competitive landscape.
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
API Access
Has API
Integrations
No details available.
Integrations
No details available.
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Stemly
Country
Singapore
Website
www.stemly.ai/
Vendor Details
Company Name
Founded
1998
Country
United States
Website
research.google/blog/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting/
Product Features
Decision Support
Application Development
Budgeting & Forecasting
Data Analysis
Decision Tree Analysis
Monte Carlo Simulation
Performance Metrics
Rules-Based Workflow
Sensitivity Analysis
Thematic Mapping
Version Control