AnomalyKiTS: Anomaly Detection Toolkit for Time Series


  • Dhaval Patel IBM Research
  • Giridhar Ganapavarapu IBM Research
  • Srideepika Jayaraman IBM Research
  • Shuxin Lin IBM Research
  • Anuradha Bhamidipaty IBM Research
  • Jayant Kalagnanam IBM Research



Anomaly Detection, Time Series, Web Service API


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.




How to Cite

Patel, D., Ganapavarapu, G., Jayaraman, S., Lin, S., Bhamidipaty, A., & Kalagnanam, J. (2022). AnomalyKiTS: Anomaly Detection Toolkit for Time Series. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13209-13211.