Time series analysis is a vital task with broad applications in various domains. However, effectively capturing cross-dimension and cross-time dependencies in non-stationary time series poses significant challenges, particularly in the context of environmental factors. The spurious correlation induced by the environment confounds the causal relationships between cross-dimension and cross-time dependencies. In this paper, we introduce a novel framework called Caformer (\underline{\textbf{Ca}}usal Trans\underline{\textbf{former}}) for time series analysis from a causal perspective. Specifically, our framework comprises three components: Dynamic Learner, Environment Learner, and Dependency Learner. The Dynamic Learner unveils dynamic interactions among dimensions, the Environment Learner mitigates spurious correlations caused by environment with a back-door adjustment, and the Dependency Learner aims to infer robust interactions across both time and dimensions. Our Caformer demonstrates consistent state-of-the-art performance across five mainstream time series analysis tasks, including long- and short-term forecasting, imputation, classification, and anomaly detection, with proper interpretability.

翻译：时间序列分析是一项具有广泛应用的至关重要任务。然而，在非平稳时间序列中有效捕捉跨维度与跨时间的依赖关系面临重大挑战，尤其是在环境因素的背景下。环境引发的伪相关混淆了跨维度与跨时间依赖之间的因果关系。本文提出了一种名为Caformer（\underline{\textbf{Ca}}usal Trans\underline{\textbf{former}}）的全新框架，从因果视角进行时间序列分析。具体而言，该框架包含三个组件：动态学习器、环境学习器和依赖学习器。动态学习器揭示维度间的动态交互，环境学习器通过后门调整消除环境导致的伪相关，依赖学习器则旨在推断跨时间与跨维度的稳健交互。我们的Caformer在五项主流时间序列分析任务（包括长短期预测、插补、分类和异常检测）中均展现出最先进的性能，并具备良好的可解释性。