Anomaly Predictor is responsible for identifying unusual patterns or behaviors in a dataset that do not conform to expected behavior. These unusual patterns are often referred to as anomalies, outliers, or exceptions. In the context of data analysis and machine learning, anomaly detection is a critical step in data preprocessing. It helps in identifying errors, noise, and extremely rare events in the data that can be used for onward processing. The primary function of an “Anomaly Detector” is to process large amounts of data and identify potential anomalies based on predefined conditions or statistical deviations from a “Pattern Manager”. It does this by learning from historical data, understanding what constitutes ‘normal’ behavior, and then flagging anything that deviates from this ‘normal’ pattern as an anomaly. “Anomaly Detector” supports a wide number of use cases across various domains including fraud detection in transactions, intrusion detection in network traffic, fault detection in operating environments, etc.