Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile

• Published in: Data mining and knowledge discovery Vol. 32; no. 1; pp. 83 - 123
• Main Authors: Yeh, Chin-Chia Michael, Zhu, Yan, Ulanova, Liudmila, Begum, Nurjahan, Ding, Yifei, Dau, Hoang Anh, Zimmerman, Zachary, Silva, Diego Furtado, Mueen, Abdullah, Keogh, Eamonn
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2018; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Bioinformatics; Chemistry and Earth Sciences; Computer Science; Data mining; Data Mining and Knowledge Discovery; Datasets; Deoxyribonucleic acid; DNA; Information Storage and Retrieval; Monitoring; Physics; Pruning; Seismology; Similarity; Statistics for Engineering; Time series; Motif discovery; Anomaly detection; Time series; Joins

The last decade has seen a flurry of research on all-pairs-similarity-search (or similarity joins ) for text, DNA and a handful of other datatypes, and these systems have been applied to many diverse data mining problems. However, there has been surprisingly little progress made on similarity joins...