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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1716
DC FieldValueLanguage
dc.contributor.authorNguyen Khoaen_US
dc.date.accessioned2023-10-24T07:37:57Z-
dc.date.available2023-10-24T07:37:57Z-
dc.date.issued2023-
dc.identifier.urihttps://epub.vgu.edu.vn/handle/dlibvgu/1716-
dc.description.abstractIn the realm of time series forecasting, the challenge often revolves around limited data availability. This thesis, titled "Self-supervised Learning For Time Series Forecasting," delves into this issue by harnessing the power of self-supervised learning techniques. The study introduces the Enhanced Rolling Windows technique, which accelerates data splitting for larger datasets. A novel linear model, drawing from RevIN and Time2Vec, is also proposed. Additionally, the research explores advanced concepts like Zero-Shot Learning and Transfer Learning, highlighting their significance in forecasting. These methodologies are particularly applied to forecast salinity datasets. In essence, the thesis presents a comprehensive strategy for time series forecasting, spotlighting the importance of self-supervised techniques in navigating the challenges of limited data.en_US
dc.language.isoenen_US
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectTime series forecastingen_US
dc.subjectSelf-supervised learningen_US
dc.subjectZero-shot learningen_US
dc.subjectTransfer learningen_US
dc.titleSelf-supervised learning for time series forecastingen_US
dc.typeThesisen_US
item.fulltextWith Fulltext-
item.grantfulltextrestricted-
item.languageiso639-1other-
Appears in Collections:Computer Science (CS)
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