The Use of bioinformatics techniques to perform time-series trend matching and prediction

dc.contributor.advisorSandrock, Carl
dc.contributor.emailmarktransell@gmail.com
dc.contributor.postgraduateTransell, Mark Marriott
dc.date.accessioned2014-03-04T11:43:59Z
dc.date.available2014-03-04T11:43:59Z
dc.date.created2014
dc.date.issued2012
dc.descriptionDissertation (MEng)--University of Pretoria, 2012.en_US
dc.description.abstractProcess operators often have process faults and alarms due to recurring failures on process equipment. It is also the case that some processes do not have enough input information or process models to use conventional modelling or machine learning techniques for early fault detection. A proof of concept for online streaming prediction software based on matching process behaviour to historical motifs has been developed, making use of the Basic Local Alignment Search Tool (BLAST) used in the Bioinformatics field. Execution times of as low as 1 second have been recorded, demonstrating that online matching is feasible. Three techniques have been tested and compared in terms of their computational effciency, robustness and selectivity, with results shown in Table 1: • Symbolic Aggregate Approximation combined with PSI-BLAST • Naive Triangular Representation with PSI-BLAST • Dynamic Time Warping Table 1: Properties of different motif-matching methods Property SAX-PSIBLAST TER-PSIBLAST DTW Noise tolerance (Selectivity) Acceptable Inconclusive Good Vertical Shift tolerance None Perfect Poor Matching speed Acceptable Acceptable Fast Match speed scaling O < O(mn) O < O(mn) O(mn) Dimensionality Reduction Tolerance Good Inconclusive Acceptable It is recommended that a method using a weighted confidence measure for each technique be investigated for the purpose of online process event handling and operator alerts. Keywords: SAX, BLAST, motif-matching, Dynamic Time Warpingen_US
dc.description.availabilityunrestricteden_US
dc.description.departmentChemical Engineeringen_US
dc.identifier.citationTransell, MM 2014, The Use of bioinformatics techniques to perform time-series trend matching and prediction, MEng dissertation, University of Pretoria, Pretoria, viewed yymmdd<http://hdl.handle.net/2263/37061>en_US
dc.identifier.urihttp://hdl.handle.net/2263/37061
dc.language.isoenen_US
dc.publisherUniversity of Pretoriaen_ZA
dc.rights© 2014 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.en_US
dc.subjectControl Engineeringen_US
dc.subjectData Miningen_US
dc.subjectSignal Processingen_US
dc.subjectSAX
dc.subjectBlast
dc.subjectMotif-matching
dc.subjectDynamic time warping
dc.subjectUCTDen_US
dc.subject.otherC14/4/163/gm
dc.titleThe Use of bioinformatics techniques to perform time-series trend matching and predictionen_US
dc.typeDissertationen_US

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