FACT: Frequency-Aware Channel-Guided Multivariate Time Series Forecasting

Sibo Qi, Xiaozhu Jing, Liuyang Zhao, Wuman Luo, Dian Zhang

Keywords: Time Series Forecasting, Multivariate Time Series, Spatiotemporal Dependencies, Fine-grained Interpretability, Deep Learning

Abstract: Forecasting Multivariate Time Series (MTS) requires capturing complex intra-channel dynamics and evolving inter-channel dependencies. However, existing methods often struggle to disentangle meaningful signals from inter-channel noise and intricate interaction patterns. To address this, we propose a novel framework that operates entirely in the frequency domain, modeling inter-channel relationships at the component level. Our approach first dynamically decomposes each time series into its constituent frequencies. An Adaptive Band Decomposition mechanism then identifies and isolates the most salient frequency components, simultaneously filtering noise and enhancing computational efficiency. This allows our model to capture time-varying inter-channel dependencies with high fidelity. Furthermore, our learning objective effectively balances accuracy against regularization constraints for both computational efficiency and interpretability. Extensive experiments on diverse, real-world datasets demonstrate that our method achieves competitive performance. Code is available at this repository: \url{https://anonymous.4open.science/r/FACT}.

Primary Area: learning on time series and dynamical systems

Submission Number: 18042