Toolkit for Time Series Anomaly Detection

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.