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What is TimesFM-3?

TimesFM-3 is an innovative foundation model for time series analysis, distinguished by its capability to deliver highly accurate multivariate forecasts with a single forward pass. With a substantial framework of 330 million parameters, this model has been pre-trained on an extensive dataset comprising both real-world and synthetic time series information, accumulating over 1 trillion time points, which significantly bolsters its effectiveness and zero-shot generalization compared to its predecessors. It excels at simultaneously forecasting multiple coevolving time series while effectively recognizing dependencies that enhance predictive accuracy without necessitating task-specific fine-tuning. Additionally, it is designed to handle various forecasting objectives, including point and quantile predictions, and takes into account both historical covariates and dynamic covariates related to future scenarios, such as planned promotions, holidays, or shifts in weather. Employing a decoder-only transformer architecture, TimesFM-3 adeptly processes sequential data in chunks of 32 time steps, utilizing alternating causal temporal attention and full variate attention to seamlessly weave together patterns across time and interconnected series. As a result, this model serves as a powerful resource for forecasting intricate, time-dependent phenomena across diverse applications, making it a significant advancement in the field of time series forecasting. Its versatility and precision open up new avenues for exploration and application in various domains.

What is Shapelets?

Unlock the potential of cutting-edge computing technology right at your fingertips. Thanks to advanced parallel processing and innovative algorithms, there's no reason to delay any further. Designed with data scientists in mind, particularly within the business sector, this comprehensive time-series platform offers unparalleled computing speed. Shapelets provides a robust array of analytical features, such as causality analysis, discord detection, motif discovery, forecasting, and clustering, among others. Users can also execute, enhance, and integrate their own algorithms within the Shapelets platform, fully harnessing the power of Big Data analytics. It seamlessly connects with various data collection and storage systems, ensuring compatibility with MS Office and other visualization applications, which simplifies the sharing of insights without requiring deep technical expertise. The user-friendly interface works in tandem with the server to deliver interactive visualizations, enabling you to effectively utilize your metadata and exhibit it through diverse modern graphical formats. Moreover, Shapelets empowers professionals in the oil, gas, and energy industries to perform real-time analyses of their operational data, thus improving decision-making processes and operational effectiveness. By leveraging Shapelets, you can turn intricate data into strategic insights that drive success and innovation in your field. This platform not only streamlines data analysis but also fosters a collaborative environment for teams to thrive.

Media

Media

Integrations Supported

Integrations Supported

Amazon S3
Apache HBase
Apache Kafka
Azure Blob Storage
Google Cloud Storage
HPE Ezmeral Data Fabric
Microsoft Excel
Microsoft Office 2021
Microsoft PowerPoint
MongoDB
Qlik Data Integration
SAS MDM
Spotfire
Strategy ONE
Tableau

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

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

Google

Date Founded

1998

Company Location

United States

Company Website

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

Company Facts

Organization Name

Shapelets

Company Location

Spain

Company Website

shapelets.io

Categories and Features

AI Models

Not specified

Foundation Models

Not specified

Categories and Features

Data Science

Not specified

Data Visualization

Not specified

Graphing

Not specified

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