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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
Title: Self-supervised learning for time series forecasting
Authors: Nguyen Khoa 
Keywords: Time series forecasting;Self-supervised learning;Zero-shot learning;Transfer learning
Issue Date: 2023
Abstract: 
In 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.
URI(1): https://epub.vgu.edu.vn/handle/dlibvgu/1716
Rights: Attribution-NonCommercial 4.0 International
Appears in Collections:Computer Science (CS)

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