Time Series Anomaly Detection Tool: Hands of Lab

Abstract

Asset Health and Monitoring is an emerging AI Application that aims to deliver efficient AI-powered solutions to various industrial problems such as anomaly detection, failure pattern analysis, etc. In this lab-based tutorial, we present a web-based time series anomaly detection tool – a new scikit-learn compatible toolkit specialized for the time series-based anomaly detection problem. The key focus of our tutorial includes the design and development of an anomaly detection pipeline, a zero-configuration interface for automated discovery of an anomaly pipeline for any given dataset (univariate and multi-variate), a set of 5 frequently used workflow empirically derived from past experiences, a scalable technique for conducting efficient pipeline execution. We extensively tested deployed anomaly detection services using multiple datasets with varying time-series data characteristics.