TODS: An Automated Time Series Outlier Detection System

• URL: http://arxiv.org/abs/2009.09822v4
• Date: Sun, 11 May 2025 00:25:45 GMT
• Title: TODS: An Automated Time Series Outlier Detection System
• Authors: Kwei-Herng Lai, Daochen Zha, Guanchu Wang, Junjie Xu, Yue Zhao, Devesh Kumar, Yile Chen, Purav Zumkhawaka, Mingyang Wan, Diego Martinez, Xia Hu,
• Abstract summary: TODS is a highly modular system that supports easy pipeline construction.<n>Tods supports 70 primitives, including data processing, time series processing, feature analysis, detection algorithms, and a reinforcement module.
• Score: 70.88663649631857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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