Anomaly Detection engines in the Veritone cognitive engine ecosystem assign a confidence value to specific entries in time-series data sets with the goal of predicting which events are anomalous.
Simply ingest time-series data that gives the engine a list of numbers representing certain events and corresponding timestamps of when they occurred. The engine will process this and return the likelihood that any given event is anomalous.
Deploy in a new or integrate into an existing application in the cloud via aiWARE GraphQL APIs, or with a subset that can be deployed on-premise via a Docker container. Learn more.
Leverage advanced anomaly detection machine learning algorithms from the Veritone managed cognitive engine ecosystem — including algorithms from Veritone, niche providers, and industry giants.