VGU RESEARCH REPOSITORY
Please use this identifier to cite or link to this item:
https://epub.vgu.edu.vn/handle/dlibvgu/1716| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Nguyen Khoa | en_US |
| dc.date.accessioned | 2023-10-24T07:37:57Z | - |
| dc.date.available | 2023-10-24T07:37:57Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | https://epub.vgu.edu.vn/handle/dlibvgu/1716 | - |
| dc.description.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. | en_US |
| dc.language.iso | en | en_US |
| dc.rights | Attribution-NonCommercial 4.0 International | * |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | * |
| dc.subject | Time series forecasting | en_US |
| dc.subject | Self-supervised learning | en_US |
| dc.subject | Zero-shot learning | en_US |
| dc.subject | Transfer learning | en_US |
| dc.title | Self-supervised learning for time series forecasting | en_US |
| dc.type | Thesis | en_US |
| item.fulltext | With Fulltext | - |
| item.grantfulltext | restricted | - |
| item.languageiso639-1 | other | - |
| Appears in Collections: | Computer Science (CS) | |
Files in This Item:
| File | Description | Size | Format | Existing users please Login |
|---|---|---|---|---|
| Self-supervised learning for time series forecasting.pdf | 6.48 MB | Adobe PDF |
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