Although transformer-based methods have achieved great success in multi-scale temporal pattern interaction modeling, two key challenges limit their further development: (1) Individual time points contain less semantic information, and leveraging attention to model pair-wise interactions may cause the information utilization bottleneck. (2) Multiple inherent temporal variations (e.g., rising, falling, and fluctuating) entangled in temporal patterns. To this end, we propose Adaptive Multi-Scale Hypergraph Transformer (Ada-MSHyper) for time series forecasting. Specifically, an adaptive hypergraph learning module is designed to provide foundations for modeling group-wise interactions, then a multi-scale interaction module is introduced to promote more comprehensive pattern interactions at different scales. In addition, a node and hyperedge constraint mechanism is introduced to cluster nodes with similar semantic information and differentiate the temporal variations within each scales. Extensive experiments on 11 real-world datasets demonstrate that Ada-MSHyper achieves state-of-the-art performance, reducing prediction errors by an average of 4.56%, 10.38%, and 4.97% in MSE for long-range, short-range, and ultra-long-range time series forecasting, respectively. Code is available at https://github.com/shangzongjiang/Ada-MSHyper.

翻译：尽管基于Transformer的方法在多尺度时序模式交互建模方面已取得巨大成功，但其进一步发展受到两个关键挑战的限制：(1) 单个时间点包含的语义信息较少，利用注意力机制建模点对点交互可能导致信息利用瓶颈。(2) 时序模式中纠缠着多种固有时间变化（例如上升、下降和波动）。为此，我们提出用于时间序列预测的自适应多尺度超图Transformer（Ada-MSHyper）。具体而言，设计了一个自适应超图学习模块，为建模组间交互提供基础；随后引入多尺度交互模块，以促进不同尺度间更全面的模式交互。此外，引入了节点与超边约束机制，以聚类具有相似语义信息的节点，并区分每个尺度内的时间变化。在11个真实世界数据集上的大量实验表明，Ada-MSHyper实现了最先进的性能，在长程、短程和超长程时间序列预测中，MSE预测误差平均分别降低了4.56%、10.38%和4.97%。代码可在 https://github.com/shangzongjiang/Ada-MSHyper 获取。