Caformer: Rethinking Time Series Analysis from Causal Perspective

• URL: http://arxiv.org/abs/2403.08572v1
• Date: Wed, 13 Mar 2024 14:28:02 GMT
• Title: Caformer: Rethinking Time Series Analysis from Causal Perspective
• Authors: Kexuan Zhang, Xiaobei Zou, Yang Tang
• Abstract summary: We introduce a novel framework called Caformer for time series analysis from a causal perspective. Our framework comprises three components: Dynamic Learner, Environment Learner, and Dependency Learner. Our Caformer demonstrates consistent state-of-the-art performance across five mainstream time series analysis tasks.
• Score: 7.354128514581098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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