ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time
  Series Forecasting
Hengyu Ye Jiadong Chen Shijin Gong Fuxin Jiang Tieying Zhang Jianjun Chen Xiaofeng Gao
The intricate nature of time series data analysis benefits greatly from the
distinct advantages offered by time and frequency domain representations. While
the time domain is superior in representing local dependencies, particularly in
non-periodic series, the frequency domain excels in capturing global
dependencies, making it ideal for series with evident periodic patterns. To
capitalize on both of these strengths, we propose ATFNet, an innovative
framework that combines a time domain module and a frequency domain module to
concurrently capture local and global dependencies in time series data.
Specifically, we introduce Dominant Harmonic Series Energy Weighting, a novel
mechanism for dynamically adjusting the weights between the two modules based
on the periodicity of the input time series. In the frequency domain module, we
enhance the traditional Discrete Fourier Transform (DFT) with our Extended DFT,
designed to address the challenge of discrete frequency misalignment.
Additionally, our Complex-valued Spectrum Attention mechanism offers a novel
approach to discern the intricate relationships between different frequency
combinations. Extensive experiments across multiple real-world datasets
demonstrate that our ATFNet framework outperforms current state-of-the-art
methods in long-term time series forecasting.