Caformer: Rethinking Time Series Analysis from Causal Perspective
<|reference_start|>Caformer: Rethinking Time Series Analysis from Causal Perspective: 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.<|reference_end|>
@article{zhang2024caformer:,
    title={Caformer: Rethinking Time Series Analysis from Causal Perspective},
    author={Kexuan Zhang, Xiaobei Zou, Yang Tang},
    journal={arXiv preprint arXiv:2403.08572},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08572},
    primaryClass={cs.LG}
}