PhD Thesis - Ahmed Shifaz - final-v2.pdf (5.36 MB)
Download fileScalable and Accurate Time Series Classification
thesis
posted on 2022-01-10, 05:22 authored by AHMED SHIFAZThis thesis focuses on time series classification, which aims to develop algorithms that learn to categorize temporally ordered data. It is an important area of machine learning research with a diverse range of applications, such as the classification of satellite images, medical and human activity data. This research addresses the lack of support for scalability and multivariate time series among state-of-the-art time series classifiers. It contributes two novel univariate algorithms that demonstrate state-of-the-art performance in accuracy while being several magnitudes faster than its competitors. It also contributes seven multivariate similarity measures and two ensembles for multivariate time series classification.
History
Campus location
AustraliaPrincipal supervisor
Geoff Ian WebbAdditional supervisor 1
Francois PetitjeanAdditional supervisor 2
Charlotte PelletierAdditional supervisor 3
Matthieu HerrmannYear of Award
2022Department, School or Centre
Data Science & Artificial IntelligenceCourse
Doctor of PhilosophyDegree Type
DOCTORATEFaculty
Faculty of Information TechnologyUsage metrics
Categories
Keywords
Multivariate Time Series ClassificationTime Series ClassificationMachine LearningEnsemble MethodsRandom ForestsSimilarity MeasuresIndependent MeasuresDependent MeasuresElastic MeasuresTime Series AnalysisKnowledge Representation and Machine LearningTime-Series AnalysisPattern Recognition and Data Mining