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 (FoundTS). 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, and deep learning methods.

Unified Pipeline

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

Paper

PVLDB 2024 Best Research Paper Nomination

arXiv

Cite us

@article{qiu2024tfb,
  title   = {TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods},
  author  = {Qiu, Xiangfei and Hu, Jilin and Zhou, Lekui and Wu, Xingjian and Du, Junyang and Zhang, Buang and Guo, Chenjuan and Zhou, Aoying and Jensen, Christian S and Sheng, Zhenli and Bin Yang},
  journal = {Proc. {VLDB} Endow.},
  year    = {2024},
  pages   = {2363 - 2377},
  volume  = {17}
}

@article{li2024foundts,
    title  = {FoundTS: 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 Qingsong Wen and Christian S. Jensen and Bin Yang},
    year   = {2024},
    eprint = {2410.11802},
    archivePrefix = {arXiv}
}