AnomalyKiTS: Anomaly Detection Toolkit for Time Series

Dhaval Patel, Giridhar Ganapavarapu, Srideepika Jayaraman, Shuxin Lin, Anuradha Bhamidipaty, Jayant Kalagnanam

AAAI 2022 pp. 13209-13211

doi:10.1609/AAAI.V36I11.21730 /aaai/2022/patel2022aaai-anomalykits/

Abstract

This demo paper presents a design and implementation of a system AnomalyKiTS for detecting anomalies from time series data for the purpose of offering a broad range of algorithms to the end user, with special focus on unsupervised/semi-supervised learning. Given an input time series, AnomalyKiTS provides four categories of model building capabilities followed by an enrichment module that helps to label anomaly. AnomalyKiTS also supports a wide range of execution engines to meet the diverse need of anomaly workloads such as Serveless for CPU intensive work, GPU for deep-learning model training, etc.

PDF AAAI Semantic Scholar

Cite

Text

Patel et al. "AnomalyKiTS: Anomaly Detection Toolkit for Time Series." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21730

Markdown

[Patel et al. "AnomalyKiTS: Anomaly Detection Toolkit for Time Series." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/patel2022aaai-anomalykits/) doi:10.1609/AAAI.V36I11.21730

BibTeX

@inproceedings{patel2022aaai-anomalykits,
  title     = {{AnomalyKiTS: Anomaly Detection Toolkit for Time Series}},
  author    = {Patel, Dhaval and Ganapavarapu, Giridhar and Jayaraman, Srideepika and Lin, Shuxin and Bhamidipaty, Anuradha and Kalagnanam, Jayant},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {13209-13211},
  doi       = {10.1609/AAAI.V36I11.21730},
  url       = {https://mlanthology.org/aaai/2022/patel2022aaai-anomalykits/}
}