KDP-MHL: Key data point-aware multi-scale hypergraph learning framework for multivariate time series classification

Multivariate Time Series Classification faces inherent challenges due to complex high-order temporal correlations among data points and redundant data that obscure discriminative patterns. Existing methods primarily focus on modeling local or pairwise interactions while ignoring the distinction between informative and redundant data points. To capture informative high-order relationships underlying multi-scale temporal patterns, we propose the Key Data Point-Aware Multi-Scale Hypergraph Learning Framework (KDP-MHL) with an encoder-decoder architecture based on hypergraph neural networks. Throughout the framework, we develop a Local-Enhanced Dynamic Hypergraph Propagation Layer that extracts local-enhanced node features for each data point and obtains multi-scale high-order temporal associations by constructing dynamic hypergraphs among multiple nodes. To reduce redundancy, a Key Data Point-Aware Module is designed in the encoder to calculate node importance based on high-order attribute features and retain the key data points. In the decoder, a Multiple Class Tokens Representation method is introduced to guide high-order interactions between multiple class tokens and key data point features through hypergraph structure, further aggregating class-specific information from selected key data points, thereby improving the representation capability. Extensive experiments on 24 UEA datasets demonstrate that our method achieves superior performance compared to state-of-the-art approaches, with 3% improvement in average accuracy.