This repository serves as a comprehensive, curated list of resources for time series forecasting and deep learning. It targets researchers, engineers, and practitioners by aggregating papers, code, datasets, benchmarks, courses, and libraries, aiming to streamline the exploration and adoption of state-of-the-art techniques in the field.

How It Works

The repository functions as a dynamic, community-driven knowledge base. It meticulously lists research papers, categorized by year, with direct links to official code repositories. It also curates relevant benchmarks, competitions, courses, blogs, and libraries, providing a centralized hub for staying abreast of advancements in deep learning for time series analysis.

Quick Start & Requirements

This repository is a curated list of resources, not a runnable software package. No installation or execution is required.

Highlighted Details

• Extensive collection of research papers from 2017 to the present, with a strong focus on recent advancements (2023-2025).
• Links to official code repositories for a significant portion of the listed papers, facilitating practical implementation.
• Categorization of resources beyond papers, including benchmarks, competitions, courses, blogs, and libraries.
• Coverage of emerging trends like Large Language Models (LLMs) for time series, Mamba architectures, and diffusion models.

Maintenance & Community

The repository is maintained by DaoSword and appears to be community-driven, with numerous contributions evident in the extensive list of papers and associated code. Links to community resources like Discord or Slack are not explicitly provided in the README.

Licensing & Compatibility

The repository itself does not have a specific license as it is a curated list of links. The licenses of the linked papers and code repositories vary, and users must consult each individual resource for licensing details and compatibility.

Limitations & Caveats

As a curated list, the repository's quality and comprehensiveness depend on ongoing community contributions. There is no direct support or guarantee of the functionality or maintenance status of the linked external resources.