Introduction
This article covers the five essential qualities of anomaly detection systems that you should know as a data practitioner.
Anomaly detection is a critical application of data science that offers excellent value in protecting businesses and creating proactiveness against threats. It can also help to detect the onset of faults in complex machines such as wind turbines through condition monitoring.
Anomaly detection solutions can range from setting simple statistical thresholds based on domain knowledge to building sophisticated machine-learning systems for real-time cases. For example, when the data is normally distributed, data points falling outside 2 to 3 standard deviations from the mean may be extracted as anomalies for further investigation.
Of course, there are tradeoffs in the different approaches to building anomaly detection systems and not all of them offer the same solution quality.