Utilisateur:UTGPT/Brouillon

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Real-time Anomaly Detection is an application of Anomaly Detection to the case of streaming data. The typical goal is to recognise anomalies with the shortest delay, highest testing power and lowest false-alarm rates. Real-time Anomaly Detection can be done in frequentist or Bayesian variant.

Background

Anomaly detection is the process of identifying rare or unusual events or patterns that deviate significantly from the expected behavior of a system or process. In other words, it involves detecting data points that are outside the normal range of values or behavior. This can be useful in a variety of applications, including fraud detection, network intrusion detection, fault detection in machinery, and many others. Anomaly detection can be approached using various techniques, including statistical methods, machine learning algorithms, and rule-based systems. The choice of technique depends on the specific use case, the available data, and the desired level of accuracy and performance.