Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • LTX Reviews & Ratings
    182 Ratings
    Company Website
  • SKU Science Reviews & Ratings
    16 Ratings
    Company Website
  • LM-Kit.NET Reviews & Ratings
    29 Ratings
    Company Website
  • Martus Reviews & Ratings
    187 Ratings
    Company Website
  • Bridgit Reviews & Ratings
    80 Ratings
    Company Website
  • Kantata Reviews & Ratings
    2,341 Ratings
    Company Website
  • Runn Reviews & Ratings
    37 Ratings
    Company Website
  • Banker's Dashboard Reviews & Ratings
    6 Ratings
    Company Website
  • Premier Construction Software Reviews & Ratings
    408 Ratings
    Company Website
  • EazyStock Reviews & Ratings
    65 Ratings
    Company Website

What is Timestral?

Timestral is a comprehensive suite of time-series foundation models accompanied by a Python inference toolkit, specifically designed to enable the creation of multivariate probabilistic forecasts derived from historical data sequences. It offers four distinct model sizes, including 5M, 28M, 123M, and 209M parameters, each capable of managing past-only, known-future, and categorical covariates, while providing quantile forecasts that can accommodate context windows of up to 32,768 time steps. Users can conveniently load pretrained weights from the Hugging Face Hub or from their local files, allowing for inference on both CPU and GPU setups. These forecasts can be integrated effortlessly into a wide array of applications, such as monitoring systems, capacity planning, energy management, supply chain logistics, financial risk analysis, and maintenance operations. Additionally, Timestral boasts zero-shot forecasting capabilities and can be customized to specific domain data using the Mistral Forge, further enhancing its versatility. This toolkit primarily targets data scientists, machine learning engineers, and collaborative teams eager to devise effective forecasting solutions; its repository code is available under the Apache 2.0 license, while the model weights are managed by a distinct Modified MIT license. With its robust and comprehensive features, Timestral is set to considerably improve the precision and effectiveness of predictive analytics across various sectors, ensuring that users can make informed decisions based on reliable forecasts. As industries increasingly rely on data-driven insights, Timestral stands out as an essential resource for achieving superior forecasting outcomes.

What is TabPFN-3.5?

TabPFN-3.5 represents a cutting-edge foundation model tailored for superior predictions on structured data, proving to be exceptionally valuable for a range of applications including churn analysis, fraud detection, pricing strategies, demand forecasting, and risk assessment, thereby allowing teams to deploy a single model across various use cases. This model efficiently handles data in its native format, managing challenges such as missing values, outliers, categorical variables, multi-table datasets, free text features, and numerous unique identifiers without necessitating any encoding, while also being capable of processing multiple measurements per row. Users benefit from the ability to input raw data directly, eliminating the need for extensive feature engineering or preprocessing, which enables them to receive high-quality, production-ready predictions right after the first prediction call. Significantly, TabPFN-3.5 performs predictions in a single forward pass, achieving an impressive balance between accuracy and speed, and is optimized for rapid inference—a critical aspect for latency-sensitive predictive tasks. Moreover, it can effectively accommodate large datasets of up to one million rows natively and offers an astonishing 20 times faster inference speed compared to earlier versions, marking a significant leap in the domain. This remarkable blend of efficiency, adaptability, and performance establishes TabPFN-3.5 as an invaluable resource for data scientists and organizations aiming to harness structured data to its fullest potential. In addition, the model's user-friendly nature simplifies the workflow, making it accessible for both seasoned experts and those newer to data science.

Media

No images available

Media

Integrations Supported

Mistral Forge

Integrations Supported

Amazon Web Services (AWS)
Databricks
Google Cloud Platform
Microsoft Azure
Model Context Protocol (MCP)
NVIDIA DRIVE
Python
SAP Cloud Platform
Snowflake

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided
Free Version

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

On-Prem

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

Mistral AI

Date Founded

2023

Company Location

France

Company Website

github.com/mistralai/timestral

Company Facts

Organization Name

Prior Labs

Date Founded

2024

Company Location

Germany

Company Website

priorlabs.ai/tabpfn-3-5

Categories and Features

AI Models

Not specified

Categories and Features

AI Models

Not specified

Foundation Models

Not specified

Popular Alternatives

TimesFM-3 Reviews & Ratings

TimesFM-3

Google

Popular Alternatives

TabFM Reviews & Ratings

TabFM

Google
MiMo-V2-Flash Reviews & Ratings

MiMo-V2-Flash

Xiaomi Technology
Solar Mini Reviews & Ratings

Solar Mini

Upstage AI
TimesFM-3 Reviews & Ratings

TimesFM-3

Google