Autoencoders for time series anomaly detection

Industrial Problems Seminar 

Parker Williams (Rivian Automotive)

Abstract

Autoencoders are a type of network designed to learn efficient encodings of data, typically for purposes of unsupervised data compression. I will outline a process to leverage autoencoders for unsupervised anomaly detection, which has become an essential tool in edge based system health monitoring. I will begin with a naive implementation and motivate an autoencoder variation from an anomaly detection perspective. We will then go through a few examples and implementation challenges encountered in the wild. We will end with broader observations on when this methodology can be effective and lessons learned from an organizational and software engineering perspective.