  Time series forecasting is a long-standing challenge due to the real-world
information is in various scenario (e.g., energy, weather, traffic, economics,
earthquake warning). However some mainstream forecasting model forecasting
result is derailed dramatically from ground truth. We believe it's the reason
that model's lacking ability of capturing frequency information which richly
contains in real world datasets. At present, the mainstream frequency
information extraction methods are Fourier transform(FT) based. However, use of
FT is problematic due to Gibbs phenomenon. If the values on both sides of
sequences differ significantly, oscillatory approximations are observed around
both sides and high frequency noise will be introduced. Therefore We propose a
novel frequency enhanced channel attention that adaptively modelling frequency
interdependencies between channels based on Discrete Cosine Transform which
would intrinsically avoid high frequency noise caused by problematic periodity
during Fourier Transform, which is defined as Gibbs Phenomenon. We show that
this network generalize extremely effectively across six real-world datasets
and achieve state-of-the-art performance, we further demonstrate that frequency
enhanced channel attention mechanism module can be flexibly applied to
different networks. This module can improve the prediction ability of existing
mainstream networks, which reduces 35.99% MSE on LSTM, 10.01% on Reformer,
8.71% on Informer, 8.29% on Autoformer, 8.06% on Transformer, etc., at a slight
computational cost ,with just a few line of code. Our codes and data are
available at https://github.com/Zero-coder/FECAM.
