  Deep learning algorithms, especially Transformer-based models, have achieved
significant performance by capturing long-range dependencies and historical
information. However, the power of convolution has not been fully investigated.
Moreover, most existing works ignore the dynamic interaction among variables
and evolutionary noise in series. Addressing these issues, we propose a
Hierarchical Memorizing Network (HMNet). In particular, a hierarchical
convolution structure is introduced to extract the information from the series
at various scales. Besides, we propose a dynamic variable interaction module to
learn the varying correlation and an adaptive denoising module to search and
exploit similar patterns to alleviate noises. These modules can cooperate with
the hierarchical structure from the perspective of fine to coarse grain.
Experiments on five benchmarks demonstrate that HMNet significantly outperforms
the state-of-the-art models by 10.6% on MSE and 5.7% on MAE. Our code is
released at https://github.com/yzhHoward/HMNet.
