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Diffusion models for time series denoising

dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorOosterlee, C.W.
dc.contributor.authorBree, Bas
dc.date.accessioned2025-04-03T14:02:06Z
dc.date.available2025-04-03T14:02:06Z
dc.date.issued2025
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/48806
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectDiffusion models are a class of generative neural network that make use of a diffusion process to generate new samples of a learned data distribution. Seperately, in time series analysis, denoising is a process we apply to noisy time series to more easily isolate trends. In this thesis we ask if diffusion models can be used to perform denoising on time series.
dc.titleDiffusion models for time series denoising
dc.type.contentBachelor Thesis
dc.rights.accessrightsOpen Access
dc.subject.courseuuWiskunde
dc.thesis.id28982


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