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2021 AAAI AAAI 2021

TODS: An Automated Time Series Outlier Detection System

Abstract

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)

🌉 Interdisciplinary Bridge — Artificial Intelligence and Data Science & Analytics and Machine Learning
🧭 Keyword Pioneer — automated pipeline
🐣 Hot Topic Early Bird — time series analysis
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Kwei-Herng Lai , Daochen Zha , Guanchu Wang , Junjie Xu , Yue Zhao , Devesh Kumar , Yile Chen , Purav Zumkhawaka , Minyang Wan , Diego Martinez , Xia Hu

Topics

Artificial Intelligence > Learning Paradigms > Transfer Learning Data Science & Analytics > Methods > Time Series Analysis Machine Learning > Learning Types > Anomaly Detection Data Science & Analytics > Applications > Anomaly Detection

Keywords

time series analysis reinforcement learning anomaly detection outlier detection time series feature engineering automated machine learning automated pipeline pipeline construction
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