Publication
KDD 2022
Tutorial

Toolkit for Time Series Anomaly Detection

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Abstract

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.

Date

Publication

KDD 2022