What is anomaly detection?
The process of identifying data points that do not conform to expected patterns or behaviors
anomaly detection explained in plain English
Anomaly detection is a technique used to find unusual or unexpected data points in a dataset. These data points, called anomalies, can indicate errors, unusual behavior, or interesting patterns that are worth exploring further.
Analogy
Anomaly detection is like a quality control inspector on a production line, who looks for products that don't meet the standard specifications and flags them for further investigation
Example
A credit card company uses anomaly detection to identify transactions that are outside of a customer's normal spending habits, in order to prevent fraudulent activity
How is anomaly detection used?
Anomaly detection is used in a variety of applications, including fraud detection, network security, and predictive maintenance, to identify unusual patterns or behaviors that may indicate a problem or opportunity
Common misconceptions about anomaly detection
Some people think that anomaly detection is only used for finding errors or problems, but it can also be used to identify new opportunities or patterns that can inform business decisions
History
Anomaly detection has its roots in statistical process control, which was developed in the early 20th century. With the advent of machine learning and big data, anomaly detection has become a key technique in many industries
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