TODS: An Automated Time Series Outlier Detection System

Authors

  • Kwei-Herng Lai Texas A&M University
  • Daochen Zha Texas A&M University
  • Guanchu Wang Texas A&M University
  • Junjie Xu Texas A&M University
  • Yue Zhao Carnegie Mellon University
  • Devesh Kumar Texas A&M University
  • Yile Chen Texas A&M University
  • Purav Zumkhawaka Texas A&M University
  • Minyang Wan Texas A&M University
  • Diego Martinez Texas A&M University
  • Xia Hu Texas A&M University

Keywords:

Time Series Outlier Detection System, Outlier Detection, Time Series, End-to-end 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)

Downloads

Published

2021-05-18

How to Cite

Lai, K.-H., Zha, D., Wang, G., Xu, J., Zhao, Y., Kumar, D., Chen, Y., Zumkhawaka, P., Wan, M., Martinez, D., & Hu, X. (2021). TODS: An Automated Time Series Outlier Detection System. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 16060-16062. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/18012