AnomalyKiTS: Anomaly Detection Toolkit for Time Series
Dhaval Patel, Giridhar Ganapavarapu, Srideepika Jayaraman, Shuxin Lin, Anuradha Bhamidipaty, Jayant Kalagnanam
[AAAI-22] Demonstrations
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.
Sessions where this paper appears
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Poster Session 3
Blue 6
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Poster Session 7
Blue 6
