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

DOI10.1007/s10618-017-0519-9zbMath1416.62534OpenAlexW2702877955MaRDI QIDQ1741368

Yan Zhu, Nurjahan Begum, Zachary Zimmerman, Chin-Chia Michael Yeh, Yifei Ding, Hoang Anh Dau, Abdullah Mueen, Diego Furtado Silva, Liudmila Ulanova, Eamonn J. Keogh

Publication date: 3 May 2019

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-017-0519-9

zbMATH Keywords

time seriesjoinsanomaly detectionmotif discovery

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Protein sequences, DNA sequences (92D20)

Related Items (8)

KNN matrix profile for knowledge discovery from time series ⋮ An ultra-fast time series distance measure to allow data mining in more complex real-world deployments ⋮ Probabilistic K -means with Local Alignment for Clustering and Motif Discovery in Functional Data ⋮ Mining subsequent trend patterns from financial time series ⋮ Introducing time series snippets: a new primitive for summarizing long time series ⋮ Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile ⋮ AURORA: A Unified fRamework fOR Anomaly detection on multivariate time series ⋮ Time is money: dynamic-model-based time series data-mining for correlation analysis of commodity sales

Uses Software

• CMU Motion Capture

Cites Work

• A disk-aware algorithm for time series motif discovery
• Nonparametric density estimation for positive time series
• Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile
• On Analytic Convolutions of Bernoulli Distributions
• Nearly optimal sparse fourier transform

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