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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2009.09822 |
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| _version_ | 1866918015962972160 |
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| author | Lai, Kwei-Herng Zha, Daochen Wang, Guanchu Xu, Junjie Zhao, Yue Kumar, Devesh Chen, Yile Zumkhawaka, Purav Wan, Mingyang Martinez, Diego Hu, Xia |
| author_facet | Lai, Kwei-Herng Zha, Daochen Wang, Guanchu Xu, Junjie Zhao, Yue Kumar, Devesh Chen, Yile Zumkhawaka, Purav Wan, Mingyang Martinez, Diego Hu, Xia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2009_09822 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | TODS: An Automated Time Series Outlier Detection System Lai, Kwei-Herng Zha, Daochen Wang, Guanchu Xu, Junjie Zhao, Yue Kumar, Devesh Chen, Yile Zumkhawaka, Purav Wan, Mingyang Martinez, Diego Hu, Xia Databases Machine Learning 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. |
| title | TODS: An Automated Time Series Outlier Detection System |
| topic | Databases Machine Learning |
| url | https://arxiv.org/abs/2009.09822 |