提出新方法，从非平稳时间序列中识别因果动态与分布变化
Causal Temporal Representation Learning with Nonstationary Sparse Transition
- 基于转移稀疏性与条件独立性约束建模
- 在合成与真实数据上显著提升分布偏移识别效果
- 适合无先验变量知识的复杂时序分析场景
因果时序表征学习（Ctrl）方法旨在揭示复杂非平稳时序序列的时序因果动态。尽管现有方法取得成功，但通常需要直接观测领域变量或假设其满足马尔可夫先验，这限制了它们在缺乏此类先验知识的实际场景中的应用。为此，本文采用符合人类直觉的转移稀疏性假设，并从理论角度给出可识别性结果。具体而言，我们探讨了在何种转移变异显著性条件下可构建模型以识别分布偏移。基于该理论结果，提出新型框架 CtrlNS，利用转移稀疏性与条件独立性约束，可靠识别分布偏移与潜在因子。在合成与真实世界数据集上的实验表明，该方法显著优于现有基线，验证了其有效性。
原文摘要 · Abstract (English)
Causal Temporal Representation Learning (Ctrl) methods aim to identify the temporal causal dynamics of complex nonstationary temporal sequences. Despite the success of existing Ctrl methods, they require either directly observing the domain variables or assuming a Markov prior on them. Such requirements limit the application of these methods in real-world scenarios when we do not have such prior knowledge of the domain variables. To address this problem, this work adopts a sparse transition assumption, aligned with intuitive human understanding, and presents identifiability results from a theoretical perspective. In particular, we explore under what conditions on the significance of the variability of the transitions we can build a model to identify the distribution shifts. Based on the theoretical result, we introduce a novel framework, Causal Temporal Representation Learning with Nonstationary Sparse Transition (CtrlNS), designed to leverage the constraints on transition sparsity and conditional independence to reliably identify both distribution shifts and latent factors. Our experimental evaluations on synthetic and real-world datasets demonstrate significant improvements over existing baselines, highlighting the effectiveness of our approach.
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