Time Series Domain Adaptation Via Latent Invariant Causal Mechanism
Cai, Ruichu Huang, Junxian Yang, Zhenhui Li, Zijian Eldele, Emadeldeen Wu, Min Sun, Fuchun
Cai, Ruichu
Huang, Junxian
Yang, Zhenhui
Li, Zijian
Eldele, Emadeldeen
Wu, Min
Sun, Fuchun
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Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
Time series domain adaptation aims to transfer the complex temporal dependence from the labeled source domain to the unlabeled target domain. Recent advances leverage the stable causal mechanism over observed variables to model the domain-invariant temporal dependence. However, modeling precise causal structures in high-dimensional data, such as videos, remains challenging. Additionally, direct causal edges may not exist among observed variables (e.g., pixels). These limitations hinder the applicability of existing approaches to real-world scenarios. To address these challenges, we find that the high-dimension time series data are generated from the low-dimension latent variables, which motivates us to model the causal mechanisms of the temporal latent process. Based on this intuition, we propose a latent causal mechanism identification framework that guarantees the uniqueness of the reconstructed latent causal structures. Specifically, we first identify latent variables by utilizing sufficient changes in historical information. Moreover, by enforcing the sparsity of the relationships of latent variables, we can achieve identifiable latent causal structures. Built on the theoretical results, we develop the Latent Causality Alignment (LCA) model that leverages variational inference, which incorporates an intra-domain latent sparsity constraint for latent structure reconstruction and an inter-domain latent sparsity constraint for domain-invariant structure reconstruction. Experiment results on eight benchmarks show a general improvement in the domain-adaptive time series classification and forecasting tasks, highlighting the effectiveness of our method in real-world scenarios.
Citation
R. Cai et al., "Time Series Domain Adaptation Via Latent Invariant Causal Mechanism," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2025.3642245
Source
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conference
Keywords
Domain Adaptation, Latent Causal Mechanism, Time Series Data
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Publisher
IEEE