Survey on time series motif discovery

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

[1]  Li Wei,et al.  Experiencing SAX: a novel symbolic representation of time series , 2007, Data Mining and Knowledge Discovery.

[2]  Eamonn J. Keogh,et al.  Finding Time Series Motifs in Disk-Resident Data , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[3]  Volker Lohweg,et al.  Shift-Invariant Feature Extraction for Time-Series Motif Discovery , 2015 .

[4]  Eamonn J. Keogh,et al.  Exact Discovery of Time Series Motifs , 2009, SDM.

[5]  Eamonn J. Keogh,et al.  Online discovery and maintenance of time series motifs , 2010, KDD.

[6]  Frédéric Bimbot,et al.  An efficient method for the unsupervised discovery of signalling motifs in large audio streams , 2011, 2011 9th International Workshop on Content-Based Multimedia Indexing (CBMI).

[7]  Eamonn J. Keogh,et al.  Mining motifs in massive time series databases , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[8]  Stephen Shaoyi Liao,et al.  Discovering original motifs with different lengths from time series , 2008, Knowl. Based Syst..

[9]  Jeremy Buhler,et al.  Finding motifs using random projections , 2001, RECOMB.

[10]  Alexander D. Poularikas,et al.  Transforms and Applications Handbook, Third Edition , 2010 .

[11]  Ali A. Ghorbani,et al.  Motif and Anomaly Discovery of Time Series Based on Subseries Join , 2010 .

[12]  Tim Oates,et al.  Motif discovery in spatial trajectories using grammar inference , 2013, CIKM.

[13]  Abdullah Mueen,et al.  Enumeration of time series motifs of all lengths , 2013, 2013 IEEE 13th International Conference on Data Mining.

[14]  Abdullah Mueen,et al.  Time series motif discovery: dimensions and applications , 2014, WIREs Data Mining Knowl. Discov..

[15]  Eamonn J. Keogh,et al.  VizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases , 2004, VLDB.

[16]  Bernhard Sick,et al.  On-line motif detection in time series with SwiftMotif , 2009, Pattern Recognit..

[17]  Tim Oates,et al.  Visualizing Variable-Length Time Series Motifs , 2012, SDM.

[18]  Gautam Shroff,et al.  Efficiently discovering frequent motifs in large-scale sensor data , 2015, CoDS '15.

[19]  Paulo J. Azevedo,et al.  Multiresolution Motif Discovery in Time Series , 2010, SDM.

[20]  Eamonn J. Keogh,et al.  Extracting Optimal Performance from Dynamic Time Warping , 2016, KDD.

[21]  V. Niennattrakul,et al.  Parameter-free motif discovery for time series data , 2012, 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[22]  Xavier Serra,et al.  Mining Melodic Patterns in Large Audio Collections of Indian Art Music , 2014, 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems.

[23]  D. Thakore,et al.  High Dimensional Data Mining in Time Series by Reducing Dimensionality and Numerosity , 2012 .

[24]  Kuniaki Uehara,et al.  Discovery of Time-Series Motif from Multi-Dimensional Data Based on MDL Principle , 2005, Machine Learning.

[25]  Reda Alhajj,et al.  Discovering all frequent trends in time series , 2004 .

[26]  Yuan Li,et al.  Finding approximate frequent patterns in streaming medical data , 2010, 2010 IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS).

[27]  Kristof Van Laerhoven,et al.  Detecting leisure activities with dense motif discovery , 2012, UbiComp.

[28]  FuTak-chung.  A review on time series data mining , 2011 .

[29]  Majid Sarrafzadeh,et al.  Toward Unsupervised Activity Discovery Using Multi-Dimensional Motif Detection in Time Series , 2009, IJCAI.

[30]  Irfan A. Essa,et al.  Detecting Subdimensional Motifs: An Efficient Algorithm for Generalized Multivariate Pattern Discovery , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[31]  Duong Tuan Anh,et al.  An efficient implementation of EMD algorithm for motif discovery in time series data , 2016, Int. J. Data Min. Model. Manag..

[32]  Joan Serrà,et al.  Particle swarm optimization for time series motif discovery , 2015, Knowl. Based Syst..

[33]  Dimitar Kazakov,et al.  SAX Discretization Does Not Guarantee Equiprobable Symbols , 2015, IEEE Transactions on Knowledge and Data Engineering.

[34]  MengChu Zhou,et al.  Efficient Motif Discovery for Large-Scale Time Series in Healthcare , 2015, IEEE Transactions on Industrial Informatics.

[35]  Vit Niennattrakul,et al.  Discovery of variable length time series motif , 2011, The 8th Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology (ECTI) Association of Thailand - Conference 2011.

[36]  Eamonn J. Keogh,et al.  Probabilistic discovery of time series motifs , 2003, KDD '03.

[37]  Eamonn J. Keogh,et al.  A symbolic representation of time series, with implications for streaming algorithms , 2003, DMKD '03.

[38]  Eamonn J. Keogh,et al.  Detecting time series motifs under uniform scaling , 2007, KDD '07.

[39]  H. K. Dai,et al.  A survey of DNA motif finding algorithms , 2007, BMC Bioinformatics.

[40]  Nassir Navab,et al.  Motif Discovery in OR Sensor Data with Application to Surgical Workflow Analysis and Activity Detection , 2009 .

[41]  Toyoaki Nishida,et al.  Learning interaction protocols by mimicking understanding and reproducing human interactive behavior , 2015, Pattern Recognit. Lett..

[42]  Paulo J. Azevedo,et al.  Mining Approximate Motifs in Time Series , 2006, Discovery Science.

[43]  Eamonn J. Keogh,et al.  iSAX: indexing and mining terabyte sized time series , 2008, KDD.

[44]  Hui Ding,et al.  Querying and mining of time series data: experimental comparison of representations and distance measures , 2008, Proc. VLDB Endow..

[45]  Elsa Ferreira Gomes,et al.  Classifying Urban Sounds using Time Series Motifs , 2015 .

[46]  Eamonn Keogh.  Exact Indexing of Dynamic Time Warping , 2002, VLDB.

[47]  Dimitrios Gunopulos,et al.  Mining Time Series Data , 2005, Data Mining and Knowledge Discovery Handbook.

[48]  Eamonn J. Keogh,et al.  Discovery of Meaningful Rules in Time Series , 2015, KDD.

[49]  Eamonn J. Keogh,et al.  Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping , 2012, KDD.

[50]  Eamonn J. Keogh,et al.  CID: an efficient complexity-invariant distance for time series , 2013, Data Mining and Knowledge Discovery.

[51]  Eamonn J. Keogh,et al.  Parameter-Free Audio Motif Discovery in Large Data Archives , 2013, 2013 IEEE 13th International Conference on Data Mining.

[52]  Tim Oates,et al.  GrammarViz 2.0: A Tool for Grammar-Based Pattern Discovery in Time Series , 2014, ECML/PKDD.

[53]  Eamonn J. Keogh,et al.  On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration , 2002, Data Mining and Knowledge Discovery.

[54]  A. Poularikas.  The transforms and applications handbook , 2000 .

[55]  Liang Ding,et al.  Migration motif: a spatial - temporal pattern mining approach for financial markets , 2009, KDD.

[56]  Siegfried Nijssen,et al.  Mining characteristic multi-scale motifs in sensor-based time series , 2013, CIKM.

[57]  Chotirat Ratanamahatana,et al.  Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery , 2015, Comput. Math. Methods Medicine.

[58]  Dimitrios Gunopulos,et al.  Iterative Incremental Clustering of Time Series , 2004, EDBT.

[59]  Fei Wang,et al.  Towards heterogeneous temporal clinical event pattern discovery: a convolutional approach , 2012, KDD.

[60]  Chotirat Ratanamahatana,et al.  Efficient Proper Length Time Series Motif Discovery , 2013, 2013 IEEE 13th International Conference on Data Mining.