Anomaly detection is not new and many techniques have been well studied. The modeling can be divided and combined into two phases: data modeling and detection modeling.
Data modeling generally consists of grouping available data in the granularity of observations we would like to detect such that it contains all of the necessary information we would like the detection model to consider.
We can identify three major types of data modeling techniques:
Point anomaly: This is similar to singular outlier detection. Each row in our dataset corresponds to an independent observation. The goal is to classify each observation as "normal" or "anomaly" or, better, to provide a numerical anomaly score.
Contextual anomaly: Each point is enriched with additional context information. A typical example is finding anomalies in a time series, where time itself represents the context. A spike of ice cream sales in January is not the same as in July. The...