An “Anomaly” is a deviation from the norm or standard. In AI, it is an unusual data point or pattern that departs significantly from the rest.
Imagine you are looking at beautiful birds flying together in the sky. Every bird follows the same direction. But suddenly, one bird starts flying in a completely different direction. That bird is an “anomaly” because it’s doing something very different from the others. In AI, we try to find these kinds of “strange” behaviors in data.
In the context of AI, the term “Anomaly” refers to data points in a dataset that do not conform to the expected behavior or trend. These anomalous data points, also known as outliers, exceptions, or surprises, can provide essential insights into a dataset as they often indicate unusual occurrences or errors.
An anomaly detection task in AI is essentially an identification task. It identifies events or behaviors that deviate from the expected pattern in a dataset. Anomalies can occur in any type of dataset - whether it be time-series data, cross-sectional data, or panel data, and in various domains, including finance, healthcare, security, and more.
In machine learning, there are different techniques used to identify anomalies - statistical methods, proximity-based methods, and deviation-based methods. Statistical methods model the data assuming it follows a particular distribution (like Gaussian or Poisson) and then look for data points far from the expected value based on the estimated model. Distance-based methods identify anomalies by computing the distance between points. Any point that is a long distance from its neighbors is considered anomalous. Clustering-based methods, on the other hand, group data into clusters and then identify points that don’t fit into any cluster as anomalies.
Several AI models facilitate anomaly detection. For instance, autoencoders are often used in deep learning-based anomaly detection. They reconstruct their inputs, and anomalies are detected based on the discrepancy between the input and its reconstruction. Other commonly used models include Isolation Forest, Local Outlier Factor, and One-Class SVM.
Anomaly detection is crucial for numerous applications, including fraud detection, intrusion detection, fault detection, system health monitoring, event detection in sensor networks, and detecting ecosystem disturbances.
Outlier, Anomaly Detection, Autoencoders, Isolation Forest, One-Class SVM, Local Outlier Factor, Clustering, Statistical Learning, Time-series Analysis.