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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/1712
Title: Domain adaptation for time series forecasting
Authors: To Quang Huy 
Keywords: Time series forecasting;Domain adaptation
Issue Date: 2023
Abstract: 
Time series forecasting is integral to various applications, from finance to healthcare, and the challenge often lies in the adaptability of prediction models across differing domains. This thesis delves into domain adaptation for time series forecasting, employing the innovative feature extractor, PatchTST. Four distinct methods are proposed and extensively analyzed: DANN (Domain-Adversarial Neural Network), DeepCORAL (Deep CORrelation ALignment), MMDA (Maximum Mean Discrepancy Adaptation), and RevIN (Reversible Instance Normalization). Each method offers different strategies to bridge the distribution gap between source and target domains. These experiments reveal that by leveraging the strength of PatchTST in feature extraction, combined with the domain adaptation capabilities of the proposed methods, substantial improvements in forecasting accuracy across diverse domains are achieved
URI(1): https://epub.vgu.edu.vn/handle/dlibvgu/1712
Rights: Attribution-NonCommercial 4.0 International
Appears in Collections:Computer Science (CS)

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