Detecting Anomalies in Streaming Data –...

There’s no question that we are seeing an increase in the availability of streaming, time-series data. Largely driven by the rise of the Internet of Things (IoT) and connected real-time data sources, we now have an enormous number of applications with sensors that produce important data that changes over time. This data presents a challenge and opportunity for businesses across every industry. How do they handle the onslaught of streaming data? How can they exploit it to make decisions in real-time? One way is to detect, in real time, when something unusual occurs. Early anomaly detection in streaming data has significant implications, yet can be very difficult to execute. It requires detectors to process data in real-time, not batches, and learn while simultaneously making predictions. In this talk, we’ll look at algorithms designed for such data and analyze the components that lead to optimal performance. We’ll also discuss a new benchmark with a labeled, real-world data set, designed to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data. How do we score in a way that rewards algorithms that detect all anomalies as soon as possible, triggers no false alarms, works with real-world time-series data across a variety of domains, and automatically adapts to changing statistics?