Rethinking general time series analysis from a frequency domain perspective

Abstract

Recently, Transformers and MLPs based models have dominated and made significant progress in time series analysis. However, these methods struggle to capture the complete periodic features to model the global dependencies of time series. To this end, we provide a new perspective to explore the potential of time series in the frequency domain. Specifically, we propose the DFT-based Amplitude-Phase Decoupling Network (APDNet), which learns the significance changes and periodicity characteristics of time series by decoupling the real and imaginary parts in the complex frequency domain. First, we propose the Fourier Temporal/Variable Interaction Module to model time dependency and dependency multiple variables, respectively. Secondly, we innovatively propose the Dimensional Expanded Feature Embedding Module (DEFEM), which expands time series into a higher-dimensional representation space to preserve more independent features. Finally, through extensive experiments on multiple real-world datasets, our APDNet achieves state-of-the-art performance in various tasks such as long-term and short-term forecasting, anomaly detection, imputation, and classification. Code is available at: https://github.com/CodeJester196/APDNet.