TODS: An Automated Time Series Outlier Detection System
This provides a tool for researchers and industrial users to streamline outlier detection in time series data, but it is incremental as it builds on existing primitives and methods.
The authors tackled the challenge of automating time series outlier detection by developing TODS, a modular system with 70 primitives for pipeline construction, which includes a GUI and data-driven searcher to automatically find suitable pipelines.
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.