Caformer: Rethinking Time Series Analysis from Causal Perspective

arXiv:2403.08572v1 Announce Type: new
Abstract: 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 …

abstract analysis applications arxiv causal challenges context correlation cs.lg dependencies domains environment environmental however perspective relationships series the environment time series type vital