Anomaly Detection

What is Anomaly Detection?

Anomaly detection is the process of identifying rare or unusual data points, events, or observations that deviate from the expected patterns in a dataset. Anomaly detection is commonly used in a variety of applications such as fraud detection, intrusion detection, and predictive maintenance.

What does Anomaly Detection do?

Anomaly detection algorithms identify patterns in the data and use them to create a model of what is considered normal behavior. The algorithm then compares each new data point to the model to determine if it is similar to normal behavior or if it is an anomaly. If an anomaly is detected, the algorithm can trigger an alert or take some other action to prevent or mitigate the impact of the anomaly.

Some benefits of using Anomaly Detection

Anomaly detection offers several benefits for detecting rare events:

  • Early Detection: Anomaly detection can detect rare events early, before they cause significant damage or disruption.

  • Automation: Anomaly detection can automate the process of detecting rare events, freeing up human resources to focus on other tasks.

  • Efficiency: Anomaly detection algorithms can process large volumes of data quickly and accurately, enabling real-time detection of rare events.

More resources to learn more about Anomaly Detection

To learn more about anomaly detection and its applications, you can explore the following resources: