Time Series Domain Adaptation via Latent Invariant Causal Mechanism

2025-02-23

AI Summary

This paper addresses core challenges in cross-domain adaptation of high-dimensional time-series data (e.g., videos): difficulty in transferring temporal dependencies, absence of direct causal edges among observed variables, and non-identifiability of high-dimensional causal structures. We propose a novel paradigm based on low-dimensional latent variable modeling to capture invariant causal mechanisms. First, we establish a theoretically grounded framework for uniquely identifying latent causal mechanisms. Second, we design a dual alignment mechanism—enforcing both intra-domain and inter-domain consistency—under sparsity constraints on latent variables, ensuring identifiability and domain invariance of the causal structure. Third, we integrate variational inference, latent causal discovery, and historical-information-driven sparse graph learning. Extensive experiments on eight benchmark datasets demonstrate significant improvements in cross-domain time-series classification and forecasting. The code is publicly available and validated on real-world scenarios.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Reasoning under Uncertainty: Causality
• Computer Vision: Multi-modal Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Transfer temporal dependence between domains
• Model latent causal mechanisms in time series
• Improve domain-adaptive classification and forecasting

Innovation

• Latent causal mechanism identification
• Sparsity constraints for identifiability
• Variational inference in LCA model