This repository serves as a comprehensive, community-curated index of resources for working with time series and sequential data. It targets data scientists, machine learning engineers, and researchers seeking tools, libraries, databases, and learning materials for time series analysis, offering a centralized point for discovery and evaluation across the domain.

How It Works

This project functions as a living, community-driven collection of categorized links. It systematically organizes resources across programming languages (Python, R, Java, JavaScript, Spark, MATLAB), specific tasks (feature engineering, anomaly detection, forecasting, visualization), databases, and educational materials, providing a structured overview of the time series ecosystem.

Highlighted Details

• Extensive Python ecosystem coverage, including libraries for anomaly detection (adtk, alibi-detect), forecasting (AutoTS, darts, greykite, neuralforecast, prophet), feature engineering (tsfresh, tsfel), and general time series ML (aeon, kats, Merlion, sktime).
• Broad language support beyond Python, featuring R packages (fable, timetk, tsibble), Java (tsml), JavaScript (echarts, Highcharts), Spark (flint), and MATLAB (hctsa).
• Comprehensive database listings, categorizing both managed services (Amazon Timestream, InfluxDB Cloud) and self-hosted solutions (InfluxDB, TimescaleDB, QuestDB, TDengine).
• Rich academic and practical learning resources, including links to papers with code, books, courses, and communities like r/TimeSeries and Gitter channels.

Maintenance & Community

The repository is a community-driven "awesome list," implying ongoing community contributions. Specific maintenance details or active community channels (like Discord/Slack) are not explicitly detailed within the provided README snippet, beyond general community links.

Licensing & Compatibility

The repository itself, as a collection of links, does not impose a specific license on the user. However, each linked project or resource is governed by its own license, which users must independently verify for compatibility, especially for commercial use.

Limitations & Caveats

As a curated list, it does not offer direct functionality or guarantee the quality, maintenance status, or licensing compliance of the linked resources. Users must independently evaluate each tool or dataset for their specific needs. The breadth means some categories might be more or less comprehensive than others.