Anomaly detection is the process of identifying the reasons for changes in data values that deviate from the norm. Anomaly is something that significantly deviates from the expected standard. Anomalous data is mostly linked to some problem or event that has affected them, such as hacking, structural or textual errors.
There are three types of anomaly detection: Supervised, semi-supervised, and unsupervised. Supervised techniques use a complete set of "normal" and "abnormal" values that are classified. Semi-supervised techniques use a dataset to create a representative norm, thus detecting anomalies. Unsupervised methods detect anomalies in an unlabeled test dataset, assuming that the majority is "normal."
In practice, anomaly detection predicts hacker attacks or which discounts or new products will increase revenue.