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2020

TODS: An Automated Time Series Outlier Detection System

Lai, Kwei-Herng, Zha, Daochen, Wang, Guanchu et al.

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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.

TODS: An Automated Time Series Outlier Detection System · Around

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Orange: From experimental machine learning to interactive data mining
Demšar, J.; Zupan, B.; Leban, G.; and Curk, T. 2004 · 2004
Earlier work this paper cites.
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning
Original
Li, Y.; Chen, Z.; Zha, D.; Zhou, K.; Jin, H.; Chen, H.; and Hu, X. 2020a · 2006
Earlier work this paper cites.
Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning
Original
Zha, D.; Lai, K.-H.; Wan, M.; and Hu, X. 2020 · 2009
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Then

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