HiPerformer：用于时间序列预测的层级置换等变Transformer
HiPerformer: Hierarchically Permutation-Equivariant Transformer for Time Series Forecasting
AI总结：
提出层级置换等变Transformer（HiPerformer），通过考虑组内与组间关系，提升多时间序列预测性能，并在真实数据上超越现有最先进方法。
摘要：
准确预测需要识别多个时间序列之间的关系。特别是对于股票价格，成分通常被划分为具有相同特征的组，而提取与这种组结构一致的关系的模型应该是有效的。因此，我们提出层级置换等变的概念，关注组内和组间成分的索引交换，以设计考虑这种组结构的模型。当预测模型具有层级置换等变性时，预测与成分的组关系一致。因此，我们提出一种层级置换等变模型，同时考虑同组内成分之间的关系和组间关系。在真实数据上进行的实验表明，所提出的方法优于现有的最先进方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
