Autoencoder Forest for Anomaly Detection from IoT Time Series

In the energy/utility context, conditional monitoring is one of the most important processes in the daily operation & maintenance of the equipment. With more and more IoT sensors being deployed on the equipment, there is an increasing demand for machine learning-based anomaly detection for conditional monitoring. In this talk, I will discuss a method we designed for anomaly detection based on a collection of autoencoders learned from time-related information. This talk will cover the whole end-to-end flow on how this method is designed, and some energy specific use cases will be used to demonstrate its performance.