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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

AnomalyKiTS: Anomaly Detection Toolkit for Time Series

February 1, 2023

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Authors

Dhaval Patel

IBM Research


Giridhar Ganapavarapu

IBM Research


Srideepika Jayaraman

IBM Research


Shuxin Lin

IBM Research


Anuradha Bhamidipaty

IBM Research


Jayant Kalagnanam

IBM Research


DOI:

10.1609/aaai.v36i11.21730


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.

Topics: AAAI

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HOW TO CITE:

Dhaval Patel||Giridhar Ganapavarapu||Srideepika Jayaraman||Shuxin Lin||Anuradha Bhamidipaty||Jayant Kalagnanam AnomalyKiTS: Anomaly Detection Toolkit for Time Series Proceedings of the AAAI Conference on Artificial Intelligence (2022) 13209-13211.

Dhaval Patel||Giridhar Ganapavarapu||Srideepika Jayaraman||Shuxin Lin||Anuradha Bhamidipaty||Jayant Kalagnanam AnomalyKiTS: Anomaly Detection Toolkit for Time Series AAAI 2022, 13209-13211.

Dhaval Patel||Giridhar Ganapavarapu||Srideepika Jayaraman||Shuxin Lin||Anuradha Bhamidipaty||Jayant Kalagnanam (2022). AnomalyKiTS: Anomaly Detection Toolkit for Time Series. Proceedings of the AAAI Conference on Artificial Intelligence, 13209-13211.

Dhaval Patel||Giridhar Ganapavarapu||Srideepika Jayaraman||Shuxin Lin||Anuradha Bhamidipaty||Jayant Kalagnanam. AnomalyKiTS: Anomaly Detection Toolkit for Time Series. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.13209-13211.

Dhaval Patel||Giridhar Ganapavarapu||Srideepika Jayaraman||Shuxin Lin||Anuradha Bhamidipaty||Jayant Kalagnanam. 2022. AnomalyKiTS: Anomaly Detection Toolkit for Time Series. "Proceedings of the AAAI Conference on Artificial Intelligence". 13209-13211.

Dhaval Patel||Giridhar Ganapavarapu||Srideepika Jayaraman||Shuxin Lin||Anuradha Bhamidipaty||Jayant Kalagnanam. (2022) "AnomalyKiTS: Anomaly Detection Toolkit for Time Series", Proceedings of the AAAI Conference on Artificial Intelligence, p.13209-13211

Dhaval Patel||Giridhar Ganapavarapu||Srideepika Jayaraman||Shuxin Lin||Anuradha Bhamidipaty||Jayant Kalagnanam, "AnomalyKiTS: Anomaly Detection Toolkit for Time Series", AAAI, p.13209-13211, 2022.

Dhaval Patel||Giridhar Ganapavarapu||Srideepika Jayaraman||Shuxin Lin||Anuradha Bhamidipaty||Jayant Kalagnanam. "AnomalyKiTS: Anomaly Detection Toolkit for Time Series". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.13209-13211.

Dhaval Patel||Giridhar Ganapavarapu||Srideepika Jayaraman||Shuxin Lin||Anuradha Bhamidipaty||Jayant Kalagnanam. "AnomalyKiTS: Anomaly Detection Toolkit for Time Series". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 13209-13211.

Dhaval Patel||Giridhar Ganapavarapu||Srideepika Jayaraman||Shuxin Lin||Anuradha Bhamidipaty||Jayant Kalagnanam. AnomalyKiTS: Anomaly Detection Toolkit for Time Series. AAAI[Internet]. 2022[cited 2023]; 13209-13211.


ISSN: 2374-3468


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