OpenTS Overview

OpenTS is a comprehensive and fair benchmarking of time series analytics, mainly including foreacsting and anaomly detection. OpenTS inlucdes Time series Forecasting Benchmark (TFB), Time series Anomaly detection Benchmark (TAB), and Time Series Foundation Model Benchmark (TSFM-Bench). OpenTS provides comprehensive time series datasets, rich time series analtycis algorithms, and a unified evaluation pipeline with various strategies and metrics.

Comprehensive Datasets

OpenTS offers comprehensive time series benchmark datasets with diverse characteristics from multiple domains and complex settings.

Rich Algorithms

OpenTS covers a diverse range of methods, including statistical learning, machine learning, deep learning methods, and time series foundation models.

Unified Pipeline

OpenTS offers a unified pipeline for evaluating time series forecasting and anomaly detection methods under a variety of experiment configuration settings.

Papers

PVLDB 2024
Best Research Paper Nomination

PVLDB 2025

SIGKDD 2025

Cite us

@inproceedings{qiu2024tfb,
    title   = {TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods},
    author  = {Xiangfei Qiu and Jilin Hu and Lekui Zhou and Xingjian Wu and Junyang Du and Buang Zhang and Chenjuan Guo and Aoying Zhou and Christian S. Jensen and Zhenli Sheng and Bin Yang},
    booktitle = {Proc. {VLDB} Endow.},
    pages   = {2363--2377},
    year    = {2024}
}

@inproceedings{qiu2025tab,
    title   = {TAB: Unified Benchmarking of Time Series Anomaly Detection Methods},
    author  = {Xiangfei Qiu and Zhe Li and Wanghui Qiu and Shiyan Hu and Lekui Zhou and Xingjian Wu and Zhengyu Li and Chenjuan Guo and Aoying Zhou and Zhenli Sheng and Jilin Hu and Christian S. Jensen and Bin Yang},
    booktitle = {Proc. {VLDB} Endow.},
    year    = {2025}
}

@inproceedings{li2024TSMF-Bench,
    title     = {TSMF-Bench: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting},
    author    = {Zhe Li and Xiangfei Qiu and Peng Chen and Yihang Wang and Hanyin Cheng and Yang Shu and Jilin Hu and Chenjuan Guo and Aoying Zhou and Christian S. Jensen and Bin Yang},
    booktitle = {{SIGKDD}},
    year      = {2025}
}