Survey on time series motif discovery

2017 ◽  
Vol 7 (2) ◽  
pp. e1199 ◽  
Author(s):  
Sahar Torkamani ◽  
Volker Lohweg
Keyword(s):  
Time Series ◽  
Motif Discovery

scholarly journals Accelerating time series motif discovery in the Intel Xeon Phi KNL processor

2019 ◽  
Vol 75 (11) ◽  
pp. 7053-7075 ◽  
Author(s):  
Ivan Fernandez ◽  
Alejandro Villegas ◽  
Eladio Gutierrez ◽  
Oscar Plata
Keyword(s):  
Time Series ◽  
Motif Discovery ◽  
Xeon Phi ◽  
Intel Xeon Phi ◽  
Intel Xeon

Face image recognition based on time series motif discovery

2012 ◽  
Author(s):  
Lianhua Chi ◽  
Yucai Feng ◽  
Hehua Chi ◽  
Ying Huang
Keyword(s):  
Time Series ◽  
Image Recognition ◽  
Motif Discovery ◽  
Face Image

scholarly journals Linear Time Motif Discovery in Time Series

2019 ◽  
pp. 136-144
Author(s):  
Xiaosheng Li ◽  
Jessica Lin
Keyword(s):  
Time Series ◽  
Motif Discovery ◽  
Linear Time

Matrix Profile XI: SCRIMP++: Time Series Motif Discovery at Interactive Speeds

2018 ◽  
Author(s):  
Yan Zhu ◽  
Chin-Chia Michael Yeh ◽  
Zachary Zimmerman ◽  
Kaveh Kamgar ◽  
Eamonn Keogh
Keyword(s):  
Time Series ◽  
Motif Discovery

Constrained Motif Discovery in Time Series

2009 ◽  
Vol 27 (4) ◽  
pp. 319-346 ◽  
Author(s):  
Yasser Mohammad ◽  
Toyoaki Nishida
Keyword(s):  
Time Series ◽  
Motif Discovery

scholarly journals SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery

2021 ◽  
Vol 11 (22) ◽  
pp. 10873
Author(s):  
Silvestro R. Poccia ◽  
K. Selçuk Candan ◽  
Maria Luisa Sapino
Keyword(s):  
Time Series ◽  
Motif Discovery ◽  
Time Series Data ◽  
Contextual Information ◽  
Multimedia Data ◽  
Series Data ◽  
Motif Search ◽  
Fixed Subset ◽  
Simplifying Assumptions ◽  
Expensive Process

A common challenge in multimedia data understanding is the unsupervised discovery of recurring patterns, or motifs, in time series data. The discovery of motifs in uni-variate time series is a well studied problem and, while being a relatively new area of research, there are also several proposals for multi-variate motif discovery. Unfortunately, motif search among multiple variates is an expensive process, as the potential number of sub-spaces in which a pattern can occur increases exponentially with the number of variates. Consequently, many multi-variate motif search algorithms make simplifying assumptions, such as searching for motifs across all variates individually, assuming that the motifs are of the same length, or that they occur on a fixed subset of variates. In this paper, we are interested in addressing a relatively broad form of multi-variate motif detection, which seeks frequently occurring patterns (of possibly differing lengths) in sub-spaces of a multi-variate time series. In particular, we aim to leverage contextual information to help select contextually salient patterns and identify the most frequent patterns among all. Based on these goals, we first introduce the contextually salient multi-variate motif (CS-motif) discovery problem and then propose a salient multi-variate motif (SMM) algorithm that, unlike existing methods, is able to seek a broad range of patterns in multi-variate time series.

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