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.

翻译：本文介绍TODS，一种面向科研与工业应用的自动化时间序列异常检测系统。TODS采用高度模块化架构，支持便捷的流水线构建。系统的核心构建单元为基元，即包含超参数的功能函数实现。TODS目前提供70种基元，涵盖数据处理、时间序列处理、特征分析、检测算法及强化模块等类别。用户可自由组合这些基元构建流水线，并通过该流水线执行端到端的异常检测任务。系统配备图形用户界面，支持通过拖拽操作灵活设计流水线。此外，系统提供数据驱动的搜索器，可根据给定数据集自动发现最优流水线配置。TODS基于Apache 2.0协议开源，项目地址为https://github.com/datamllab/tods。