Toolkit for Time Series Anomaly Detection
Dhaval J. Patel, Dzung T. Phan, Markus Mueller, Amaresh Rajasekharan · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · 2022
Time series anomaly detection is an interesting practical problem that mostly falls into unsupervised learning segment. There has been continuous stream of work being published in top-tier data mining and machine learning conferences. We invented many anomaly algorithms, procedures, and applications while working on real industrial application settings. This tutorial presents a design and implementation of a scikit-compatible system 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.