  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.
