Time Series Library is a Python library for evaluating and developing deep learning models across five mainstream time series tasks: long- and short-term forecasting, imputation, anomaly detection, and classification.
The library addresses the need for a standardized codebase to benchmark advanced deep time series models and develop new ones. It provides baseline implementations of various deep learning architectures for time series analysis, allowing researchers to compare approaches across common datasets and evaluation protocols. The tool supports both traditional forecasting paradigms and newer approaches like forecasting with exogenous variables through its TimeXer implementation, as well as zero-shot forecasting capabilities for large time series models.
Researchers developing or evaluating deep time series models should consider this library as a foundation for benchmarking work. It suits projects requiring standardized evaluation across multiple time series tasks rather than single-purpose forecasting. The library's comprehensive model implementations enable quick prototyping and comparison. However, the maintainers note that many benchmarks included may no longer reflect current research progress, and they recommend seeking newer benchmarks for evaluating state-of-the-art effectiveness. The project has also released a companion repository focused on large time series models with a distinct pretrain-finetuning paradigm for those specifically interested in that direction.
Development activity shows the project is in maintenance mode with limited active feature additions due to maintainer bandwidth constraints. Recent work has focused on infrastructure improvements including updated dependencies and Docker deployment support. The library has incorporated emerging architectures like Mamba and introduced new evaluation frameworks such as the Accuracy Law for characterizing forecasting task objectives. Documentation has been enhanced with better organization for new users.
