Revolutionizing Time Series Data Preprocessing with a Novel Cycling Layer in Self-Attention Mechanisms

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dc.contributor.advisorYang, Zijiang
dc.contributor.authorChen, Jiyan
dc.date.accessioned2024-07-18T21:20:51Z
dc.date.available2024-07-18T21:20:51Z
dc.date.copyright2024-04-12
dc.date.issued2024-07-18
dc.date.updated2024-07-18T21:20:50Z
dc.degree.disciplineInformation Systems and Technology
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractThis thesis presents a novel method for improving time series data preprocessing by incorporating a cycling layer into self-attention mechanisms. Traditional techniques often struggle to capture the cyclical nature of time series data, impacting predictive model accuracy. By integrating a cycling layer, this thesis aims to enhance the ability of models to recognize and utilize cyclical patterns within datasets, exemplified by the Jena Climate dataset from the Max Planck Institute for Biogeochemistry. Empirical results demonstrate that the proposed method not only improves the accuracy of forecasts but also increases model fitting speed compared to conventional approaches. This thesis contributes to the advancement of time series analysis by offering a more effective preprocessing technique.
dc.identifier.urihttps://hdl.handle.net/10315/42149
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectInformation technology
dc.subject.keywordsMachine learning
dc.subject.keywordsAutoencoder
dc.subject.keywordsData preprocessing
dc.subject.keywordsAttention model
dc.subject.keywordsUnsupervised learning
dc.subject.keywordsWeather prediction
dc.subject.keywordsRegresssion model
dc.titleRevolutionizing Time Series Data Preprocessing with a Novel Cycling Layer in Self-Attention Mechanisms
dc.typeElectronic Thesis or Dissertation

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