YTSag

YTSag is an open-source software framework for time-series analysis and forecasting. It provides a cohesive environment for data ingestion, preprocessing, feature engineering, model training and evaluation, and visualization. The design emphasizes modularity, reproducibility, and scalability, enabling users to build complex workflows over large datasets.

The core engine is implemented in C++ for performance, with bindings that expose the functionality to Python and R. It supports common data formats such as CSV, Parquet, and JSON, and it includes a library of forecasting and anomaly-detection algorithms, ranging from classical statistical models to machine learning-based approaches. Users can assemble pipelines, specify cross-validation and backtesting schemes, and track experiments with built-in logging and visualization tools. The framework also provides utilities for feature extraction, seasonal decomposition, and performance metrics, facilitating rapid prototyping and evaluation of time-series solutions.

YTSag originated as a community-driven project in the mid-2010s, developed by researchers and practitioners who sought a unified toolkit for time-series work. The name YTSag is the project’s identifier and has been treated as a short, pronounceable trademark rather than a fixed acronym.

The project is maintained by an international community of contributors and is released under an open-source license. Governance and contribution guidelines are documented in the official repository, and users participate through code contributions, issue tracking, and discussion forums.

YTSag has seen adoption in academia and industry pilots, particularly for forecasting, anomaly detection, and evaluation of time-series experiments. Documentation and examples are provided to help new users begin with data preparation, model selection, and result interpretation.