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Description
TabPFN-3.5 is an advanced foundation model specifically designed for achieving top-tier predictions on structured data, making it highly effective for a variety of tasks such as churn analysis, fraud detection, pricing strategies, demand forecasting, and risk assessment, thus enabling teams to utilize a single model for diverse applications. The model seamlessly processes data in its original form, adeptly managing issues like missing values, outliers, categorical data, multi-table datasets, free text features, and thousands of unique identifiers without requiring any encoding, while also accommodating numerous measurements per row. Users have the convenience of inputting raw data without the need for extensive feature engineering or preprocessing, allowing them to obtain high-quality, production-ready predictions immediately after the initial prediction call. Notably, TabPFN-3.5 generates predictions in a single forward pass, striking an optimal balance between accuracy and speed, and is optimized for quick inference, which is crucial for latency-sensitive predictive applications. Furthermore, it can efficiently handle large-scale datasets of up to one million rows natively and boasts a remarkable 20 times faster inference speed compared to its predecessors, making it a significant advancement in the field. This combination of efficiency, versatility, and performance positions TabPFN-3.5 as a powerful tool for data scientists and organizations seeking to leverage structured data effectively.
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
Amazon Web Services (AWS)
Databricks
Google Cloud Platform
Microsoft Azure
Model Context Protocol (MCP)
NVIDIA DRIVE
Python
SAP Cloud Platform
Snowflake
Integrations
Amazon Web Services (AWS)
Databricks
Google Cloud Platform
Microsoft Azure
Model Context Protocol (MCP)
NVIDIA DRIVE
Python
SAP Cloud Platform
Snowflake
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
Prior Labs
Founded
2024
Country
Germany
Website
priorlabs.ai/tabpfn-3-5
Vendor Details
Company Name
Founded
1998
Country
United States
Website
research.google/blog/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting/