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.

Caformer 是一种从因果性角度进行时间序列分析的新框架，该框架包括动态学习器、环境学习器和依赖关系学习器，能够有效地捕捉跨维度和跨时间的依赖关系，并解决环境因素引起的伪相关问题。该方法在长期和短期预测、补全、分类和异常检测等五个主流时间序列分析任务中表现出一致的最先进性能和合适的解释性。