Self-Supervised Autoregressive Domain Adaptation for Time Series Data

Self-Supervised Autoregressive Domain Adaptation for Time Series Data

IEEE Transactions on Neural Networks and Learning Systems

10.1109/TNNLS.2022.3183252

http://dx.doi.org/10.1109/tnnls.2022.3183252

Mohamed Ragab, Emadeldeen Eldele, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Xiaoli Li

Computer Science Applications, Software, Computer Networks and Communications, artificial intelligence

23 June 2022

Ragab, M., Eldele, E., Chen, Z., Wu, M., Kwoh, C.-K., & Li, X. (2022). Self-Supervised Autoregressive Domain Adaptation for Time Series Data. IEEE Transactions on Neural Networks and Learning Systems, 1–11. https://doi.org/10.1109/tnnls.2022.3183252

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 large-scale dataset (i.e., ImageNet) for the 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. Last, most of 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 Auto-Regressive Domain Adaptation (SLARDA) framework. In particular, we first design a self-supervised learning module which utilizes forecasting as an auxiliary task to improve the transferability of the source features. Second, we propose a novel autoregressive domain adaptation technique that incorporates temporal dependency of both source and target features during domain alignment. Finally, we develop an ensemble teacher model to align the class-wise distribution in the target domain via confident pseudo labeling approach. Extensive experiments have been conducted on three real-world time series applications with 30 cross-domain scenarios. Results demonstrate that our proposed SLARDA method significantly outperforms the state-of-the-art approaches for time series domain adaptation.

Publisher Copyright

This research / project is supported by the A*STAR - AME Programmatic Funds
Grant Reference no. : A20H6b0151

This research / project is supported by the A*STAR - Career Development Award
Grant Reference no. : C210112046

This research / project is supported by the A*STAR - SINGA Scholarship
Grant Reference no. : N.A

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