TODS: An Automated Time Series Outlier Detection System
Abstract
We present TODS, an automated Time Series Outlier Detection System for research and industrial applications. TODS is a highly modular system that supports easy pipeline construction. The basic building block of TODS is primitive, which is an implementation of a function with hyperparameters. TODS currently supports 70 primitives, including data processing, time series processing, feature analysis, detection algorithms, and a reinforcement module. Users can freely construct a pipeline using these primitives and perform end- to-end outlier detection with the constructed pipeline. TODS provides a Graphical User Interface (GUI), where users can flexibly design a pipeline with drag-and-drop. Moreover, a data-driven searcher is provided to automatically discover the most suitable pipelines given a dataset. TODS is released under Apache 2.0 license at https://github.com/datamllab/tods. A video is available on YouTube (https://youtu.be/JOtYxTclZgQ)
Cite
Text
Lai et al. "TODS: An Automated Time Series Outlier Detection System." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.18012Markdown
[Lai et al. "TODS: An Automated Time Series Outlier Detection System." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/lai2021aaai-tods/) doi:10.1609/AAAI.V35I18.18012BibTeX
@inproceedings{lai2021aaai-tods,
title = {{TODS: An Automated Time Series Outlier Detection System}},
author = {Lai, Kwei-Herng and Zha, Daochen and Wang, Guanchu and Xu, Junjie and Zhao, Yue and Kumar, Devesh and Chen, Yile and Zumkhawaka, Purav and Wan, Minyang and Martinez, Diego and Hu, Xia Ben},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2021},
pages = {16060-16062},
doi = {10.1609/AAAI.V35I18.18012},
url = {https://mlanthology.org/aaai/2021/lai2021aaai-tods/}
}