SIMIT: Subjectively Interesting Motifs in Time Series

SIMIT: Subjectively Interesting Motifs in Time Series

Numerical time series data are pervasive, originating from sources as diverse as wearable devices, medical equipment, to sensors in industrial plants. In many cases, time series contain interesting information in terms of subsequences that recur in approximate form, so-called <i>motifs</i&g...

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Bibliographic Details
Main Authors: Junning Deng, Jefrey Lijffijt, Bo Kang, Tijl De Bie
Format: Article
Language:English
Published: MDPI AG 2019-06-01
Series:Entropy
Subjects:
time series
motif detection
information theory
subjective interestingness
pattern mining
exploratory data mining
Online Access:https://www.mdpi.com/1099-4300/21/6/566

Internet

https://www.mdpi.com/1099-4300/21/6/566

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