C22MP: the marriage of catch22 and the matrix profile creates a fast, efficient and interpretable anomaly detector

• Published in: Knowledge and information systems 2024-08, Vol.66 (8), p.4789-4823
• Main Authors: Tafazoli, Sadaf, Lu, Yue, Wu, Renjie, Srinivas, Thirumalai Vinjamoor Akhil, Dela Cruz, Hannah, Mercer, Ryan, Keogh, Eamonn
• Format: Article
• Language: English
• Subjects: Algorithms; Computer Science; Data mining; Data Mining and Knowledge Discovery; Data structures; Database Management; Euclidean geometry; Feature extraction; Information Storage and Retrieval; Information Systems and Communication Service; Information Systems Applications (incl.Internet); IT in Business; Regular Paper; Shape recognition; Time series
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-024-02107-5

Many time series data mining algorithms work by reasoning about the relationships the conserved shapes of subsequences. To facilitate this, the Matrix Profile is a data structure that annotates a time series by recording each subsequence’s Euclidean distance to its nearest neighbor. In recent years, the community has shown that using the Matrix Profile it is possible to discover many useful properties of a time series, including repeated behaviors (motifs), anomalies, evolving patterns, regimes, etc. However, the Matrix Profile is limited to representing the relationship between the subsequence’s shapes . It is understood that, for some domains, useful information is conserved not in the subsequence’s shapes, but in the subsequence’s features . In recent years, a new set of features for time series called catch22 has revolutionized feature-based mining of time series. Combining these two ideas seems to offer many possibilities for novel data mining applications; however, there are two difficulties in attempting this. A direct application of the Matrix Profile with the catch22 features would be prohibitively slow. Less obviously, as we will demonstrate, in almost all domains, using all twenty-two of the catch22 features produces poor results, and we must somehow select the subset appropriate for the domain. In this work, we introduce novel algorithms to solve both problems and demonstrate that, for most domains, the proposed C 22 MP is a state-of-the-art anomaly detector.