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Description

Autobox is the most user-friendly solution for forecasting available today. Tailored for both beginners and seasoned professionals, it allows users to input their data and generate forecasts with expert-level accuracy. Regardless of your current forecasting technique, Autobox enhances your precision in predictions significantly. This innovative tool has been recognized as the “best-dedicated forecasting program” in the esteemed Principles of Forecasting textbook and has transitioned into an online platform. The unique methodology employed by AFS does not confine data to a rigid model or a small selection of models, enabling Autobox to optimally integrate historical data and causal factors while addressing level shifts, local time trends, pulses, and seasonal variations as needed. The Autobox engine is adept at uncovering new causal variables by analyzing patterns within historical forecast errors and outliers, often revealing causal factors that users may have been unaware of, such as promotions, holidays, and day-of-the-week influences. This capability allows users to harness a broader range of insights, ultimately leading to more refined and actionable forecasts.

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

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

Automatic Forecasting Systems

Country

United States

Website

autobox.com/cms/index.php/products/autobox

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

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

Product Features

Product Features

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