论文标题
TODS：自动时间序列离群值检测系统
TODS: An Automated Time Series Outlier Detection System
论文作者
论文摘要
我们介绍TODS，这是一种用于研究和工业应用的自动化时间序列离群检测系统。 TODS是一个高度模块化的系统，可支持简单的管道构建。 TOD的基本构建块是原始的，它是使用超参数的函数的实现。 TOD当前支持70个原始素，包括数据处理，时间序列处理，功能分析，检测算法和增强模块。用户可以使用这些原语自由地构建管道，并使用构造的管道执行最终的异常值检测。 TODS提供了一个图形用户界面（GUI），用户可以在其中灵活设计带有拖放的管道。此外，还提供了数据驱动的搜索器，以自动发现给定数据集的最合适的管道。 TODS在https://github.com/datamllab/tods的Apache 2.0许可下发布。
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.
