HiPerformer: Hierarchically Permutation-Equivariant Transformer for Time
  Series Forecasting
Ryo Umagami Yu Ono Yusuke Mukuta Tatsuya Harada
It is imperative to discern the relationships between multiple time series
for accurate forecasting. In particular, for stock prices, components are often
divided into groups with the same characteristics, and a model that extracts
relationships consistent with this group structure should be effective. Thus,
we propose the concept of hierarchical permutation-equivariance, focusing on
index swapping of components within and among groups, to design a model that
considers this group structure. When the prediction model has hierarchical
permutation-equivariance, the prediction is consistent with the group
relationships of the components. Therefore, we propose a hierarchically
permutation-equivariant model that considers both the relationship among
components in the same group and the relationship among groups. The experiments
conducted on real-world data demonstrate that the proposed method outperforms
existing state-of-the-art methods.