“…Simple forms of anomaly detection consist of out-of-limits (OOL) approaches which use predefined thresholds and raw data values to detect anomalies. A myriad of other anomaly detection techniques have been introduced and explored as potential improvements over OOL approaches, such as clustering-based approaches [15,24,28], nearest neighbors approaches [3,6,23,25], expert systems [7,34,36,43], and dimensionality reduction approaches [14,39,45], among others. These approaches represent a general improvement over OOL approaches and have been shown to be effective in a variety of use cases, yet each has its own disadvantages related to parameter specification, interpretability, generalizability, or computational expense [9,16] (see [9] for a survey of anomaly detection approaches).…”