Time Series Domain Adaptation via Latent Invariant Causal Mechanism
Journal:
arXiv
Published Date:
Feb 23, 2025
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. Codes are available at
https://github.com/DMIRLAB-Group/LCA.
