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1、6DiscreteWaveletTransform-BasedTimeSeriesAnalysisandMiningPIMWADEECHAOVALIT,NationalScienceandTechnologyDevelopmentAgencyARYYAGANGOPADHYAY,GEORGEKARABATIS,andZHIYUANCHEN,UniversityofMaryland,BaltimoreCountyTimeseriesarerecordedvaluesofaninterestingphenomenonsuchasstockprices
2、,householdincomes,orpatientheartratesoveraperiodoftime.Timeseriesdataminingfocusesondiscoveringinterestingpatternsinsuchdata.Thisarticleintroducesawavelet-basedtimeseriesdataanalysistointerestedreaders.Itprovidesasystematicsurveyofvariousanalysistechniquesthatusediscretewave
3、lettransformation(DWT)intimeseriesdatamining,andoutlinesthebene?tsofthisapproachdemonstratedbypreviousstudiesperformedondiverseapplicationdomains,includingimageclassi?cation,multimediaretrieval,andcomputernetworkanomalydetection.CategoriesandSubjectDescriptors:A.1[Introducto
4、ryandSurvey];G.3[ProbabilityandStatistics]:Timeseriesanalysis;H.2.8[DatabaseManagement]:DatabaseApplications—Datamining;I.5.4[PatternRecognition]:Applications—Signalprocessing,waveformanalysisGeneralTerms:Algorithms,Experimentation,Measurement,PerformanceAdditionalKeyWordsan
5、dPhrases:Classi?cation,clustering,anomalydetection,similaritysearch,predic-tion,datatransformation,dimensionalityreduction,noise?ltering,datacompressionACMReferenceFormat:Chaovalit,P.,Gangopadhyay,A.,Karabatis,G.,andChen,Z.2011.Discretewavelettransform-basedtimeseriesanalysi
6、sandmining.ACMComput.Surv.43,2,Article6(January2011),37pages.DOI=10.1145/1883612.1883613http://doi.acm.org/10.1145/1883612.18836131.INTRODUCTIONAtimeseriesisasequenceofdatathatrepresentrecordedvaluesofaphenomenonovertime.Timeseriesdataconstitutesalargeportionofthedatastoredi
7、nrealworlddatabases[Agrawaletal.1993].Timeseriesdataappearinmanyapplicationdomains,suchasin?nancial,meteorological,medical,socialsciences,computernetworks,andbusiness.Timeseriesarederivedfromrecordingobservationsofvarioustypesofphe-nomena,forexample,temperature,stockprices,h
8、ouseholdincome,patientheartrates,numberofbitstransferred,productsalesvolume
