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ease
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support

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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.

Description

evoML enhances the efficiency of developing high-quality machine learning models by simplifying and automating the comprehensive data science process, enabling the conversion of raw data into meaningful insights in mere days rather than several weeks. It takes charge of vital tasks such as automatic data transformation that identifies anomalies and rectifies imbalances, employs genetic algorithms for feature engineering, conducts parallel evaluations of multiple model candidates, optimizes using multi-objective criteria based on custom metrics, and utilizes GenAI technology for generating synthetic data, which is especially useful for swift prototyping while adhering to data privacy regulations. Users maintain complete ownership of and can modify the generated model code, facilitating smooth deployment as APIs, databases, or local libraries, thereby preventing vendor lock-in and promoting clear, auditable workflows. Additionally, evoML equips teams with user-friendly visualizations, interactive dashboards, and detailed charts to detect patterns, outliers, and anomalies across various applications, including anomaly detection, time-series forecasting, and fraud prevention. With its robust features, evoML not only accelerates the modeling process but also empowers users to make data-driven decisions with confidence.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

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

Google

Founded

1998

Country

United States

Website

research.google/blog/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting/

Vendor Details

Company Name

TurinTech AI

Founded

2018

Country

United Kingdom

Website

www.turintech.ai/evoml

Product Features

Product Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

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