  In time series forecasting, effectively disentangling intricate temporal
patterns is crucial. While recent works endeavor to combine decomposition
techniques with deep learning, multiple frequencies may still be mixed in the
decomposed components, e.g., trend and seasonal. Furthermore, frequency domain
analysis methods, e.g., Fourier and wavelet transforms, have limitations in
resolution in the time domain and adaptability. In this paper, we propose
D-PAD, a deep-shallow multi-frequency patterns disentangling neural network for
time series forecasting. Specifically, a multi-component decomposing (MCD)
block is introduced to decompose the series into components with different
frequency ranges, corresponding to the "shallow" aspect. A
decomposition-reconstruction-decomposition (D-R-D) module is proposed to
progressively extract the information of frequencies mixed in the components,
corresponding to the "deep" aspect. After that, an interaction and fusion (IF)
module is used to further analyze the components. Extensive experiments on
seven real-world datasets demonstrate that D-PAD achieves the state-of-the-art
performance, outperforming the best baseline by an average of 9.48% and 7.15%
in MSE and MAE, respectively.
