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WIREs Data Mining Knowl Discov
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Survey on time series motif discovery

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Last decades witness a huge growth in medical applications, genetic analysis, and in performance of manufacturing technologies and automatised production systems. A challenging task is to identify and diagnose the behavior of such systems, which aim to produce a product with desired quality. In order to control the state of the systems, various information is gathered from different types of sensors (optical, acoustic, chemical, electric, and thermal). Time series data are a set of real‐valued variables obtained chronologically. Data mining and machine learning help derive meaningful knowledge from time series. Such tasks include clustering, classification, anomaly detection and motif discovery. Motif discovery attempts to find meaningful, new, and unknown knowledge from data. Detection of motifs in a time series is beneficial for, e.g., discovery of rules or specific events in a signal. Motifs provide useful information for the user in order to model or analyze the data. Motif discovery is applied to various areas as telecommunication, medicine, web, motion‐capture, and sensor networks. This contribution provides a review of the existing publications in time series motif discovery along with advantages and disadvantages of existing approaches. Moreover, the research issues and missing points in this field are highlighted. The main objective of this focus article is to serve as a glossary for researchers in this field. WIREs Data Mining Knowl Discov 2017, 7:e1199. doi: 10.1002/widm.1199 This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining Technologies > Computational Intelligence Technologies > Machine Learning
A discovered motif of length 2050 samples (green and red) at two different locations.
[ Normal View | Magnified View ]
Example of ill‐known patterns: two similar patterns with (a) noise, (b) size and time scaling, (c) amplitude scaling, and (d) time shifting.
[ Normal View | Magnified View ]

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Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining
Technologies > Computational Intelligence
Technologies > Machine Learning
Algorithmic Development > Spatial and Temporal Data Mining

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