Please use this identifier to cite or link to this item:
Title: Self-supervised autoregressive domain adaptation for time series data
Authors: Ragab, Mohamed
Eldele, Emadeldeen
Chen, Zhenghua
Wu, Min
Kwoh, Chee Keong
Li, Xiaoli
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Ragab, M., Eldele, E., Chen, Z., Wu, M., Kwoh, C. K. & Li, X. (2022). Self-supervised autoregressive domain adaptation for time series data. EEE Transactions On Neural Networks and Learning Systems, 1-11.
Project: A20H6b0151
Journal: EEE Transactions on Neural Networks and Learning Systems
Abstract: Unsupervised domain adaptation (UDA) has successfully addressed the domain shift problem for visual applications. Yet, these approaches may have limited performance for time series data due to the following reasons. First, they mainly rely on the large-scale dataset (i.e., ImageNet) for source pretraining, which is not applicable for time series data. Second, they ignore the temporal dimension on the feature space of the source and target domains during the domain alignment step. Finally, most of the prior UDA methods can only align the global features without considering the fine-grained class distribution of the target domain. To address these limitations, we propose a SeLf-supervised AutoRegressive Domain Adaptation (SLARDA) framework. In particular, we first design a self-supervised (SL) learning module that uses forecasting as an auxiliary task to improve the transferability of source features. Second, we propose a novel autoregressive domain adaptation technique that incorporates temporal dependence of both source and target features during domain alignment. Finally, we develop an ensemble teacher model to align class-wise distribution in the target domain via a confident pseudo labeling approach. Extensive experiments have been conducted on three real-world time series applications with 30 cross-domain scenarios. The results demonstrate that our proposed SLARDA method significantly outperforms the state-of-the-art approaches for time series domain adaptation. Our source code is available at:
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2022.3183252
Schools: School of Computer Science and Engineering 
Organisations: Institute for Infocomm Research, Centre for Frontier AI Research (CFAR), A*STAR
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

Citations 50

Updated on Jul 15, 2024

Web of ScienceTM
Citations 20

Updated on Oct 28, 2023

Page view(s)

Updated on Jul 14, 2024

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.