Some Preliminariesīefore we get started with anomaly detection in scikit-learn, let’s first go over some prerequisites we need to set up and a few things to keep in mind throughout our discussion. In this blog post, we’ll explore ways to use anomaly detection techniques in the popular Python library scikit-learn (sklearn) together with GridDB, an open-source IoT-optimized time-series database.

This allows anomaly detection to run at scale and meet the demands of data-intensive, distributed systems like IoT and big data applications. Machine learning techniques help automate the process of finding and handling outliers. These low-probability items or events are typically called outliers (although they’re also referred to as novelties in some contexts). In a nutshell, anomaly detection identifies unusual or unexpected patterns in datasets. Each time you get an email alerting you about some unusual login activity in one of your online accounts, you’re seeing the process of anomaly detection in action.Īnomaly detection plays a key role in making sure modern systems run smoothly and securely, covering many different fields and use cases such as fraud detection, system health monitoring, intrusion detection, fault detection, quality assurance, and data mining.

What is anomaly in database