Deep Learning Approaches for Multivariate Time Series: Advances in Feature Selection, Classification, and Forecasting

dc.contributor.advisorTran, Son
dc.contributor.advisorHamdi, Shah Muhammad
dc.contributor.authorAlshammari, Khaznah Raghyan
dc.date.accessioned2024-12-22T15:04:04Z
dc.date.issued2024
dc.description.abstractIn this work, we present the latest developments and advancements in the machine learning-based prediction and feature selection of multivariate time series (MVTS) data. MVTS data, which involves multiple interrelated time series, presents significant challenges due to its high dimensionality, complex temporal dependencies, and inter-variable relationships. These challenges are critical in domains such as space weather prediction, environmental monitoring, healthcare, sensor networks, and finance. Our research addresses these challenges by developing and implementing advanced machine-learning algorithms specifically designed for MVTS data. We introduce innovative methodologies that focus on three key areas: feature selection, classification, and forecasting. Our contributions include the development of deep learning models, such as Long Short-Term Memory (LSTM) networks and Transformer-based architectures, which are optimized to capture and model complex temporal and inter-parameter dependencies in MVTS data. Additionally, we propose a novel feature selection framework that gradually identifies the most relevant variables, enhancing model interpretability and predictive accuracy. Through extensive experimentation and validation, we demonstrate the superior performance of our approaches compared to existing methods. The results highlight the practical applicability of our solutions, providing valuable tools and insights for researchers and practitioners working with high-dimensional time series data. This work advances the state of the art in MVTS analysis, offering robust methodologies that address both theoretical and practical challenges in this field.
dc.format.extent125
dc.identifier.urihttps://hdl.handle.net/20.500.14154/74349
dc.language.isoen_US
dc.publisherNew Mexico State University
dc.subjectData Mining
dc.subjectMachine Learning
dc.subjectDeep Learning
dc.subjectTransformers
dc.subjectMultivariate Time Series (MVTS) Data
dc.subjectFeature Selection
dc.subjectClassification
dc.subjectForecasting
dc.subjectLong Short-Term Memory (LSTM) Networks
dc.subjectSpace Weather Prediction
dc.titleDeep Learning Approaches for Multivariate Time Series: Advances in Feature Selection, Classification, and Forecasting
dc.typeThesis
sdl.degree.departmentDepartment of Computer Science
sdl.degree.disciplineComputer Science
sdl.degree.grantorNew Mexico State University
sdl.degree.nameDoctor of Philosophy (PhD)

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