Sun Yao, Zuo Dongshi, Gao Jing
Department of Computer Science and Technology, College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010011, China.
Inner Mongolia Autonomous Region Big Data Center, Hohhot 010091, China.
Sensors (Basel). 2025 Aug 14;25(16):5043. doi: 10.3390/s25165043.
Time series clustering finds wide application but is often limited by data quality and the inherent limitations of existing methods. Compared to high-dimensional structured data like images, the low-dimensional features of time series contain less information, and endogenous noise can easily obscure important patterns. When dealing with massive time series data, existing clustering methods often focus on mining associations between sequences. However, ideal clustering results are difficult to achieve by relying solely on pairwise association analysis in the presence of noise and information scarcity. To address these issues, we propose a framework called Patch Graph Mamba (PG-Mamba). For the first time, the spatio-temporal patterns of a single sequence are explored by dividing the time series into multiple patches and constructing a spatio-temporal graph (STG). In this graph, these patches serve as nodes, connected by both spatial and temporal edges. By leveraging Mamba-driven long-range dependency learning and a decoupled spatio-temporal graph attention mechanism, our framework simultaneously captures temporal dynamics and spatial relationships and, thus, enabling the effective extraction of key information from time series. Furthermore, a spatio-temporal adjacency matrix reconstruction loss is introduced to mitigate feature space perturbations induced by the clustering loss. Experimental results demonstrate that PG-Mamba outperforms state-of-the-art methods, offering new insights into time series clustering tasks. Across the 33 datasets of the UCR time series archive, PG-Mamba achieved the highest average rank of 3.606 and secured the most first-place rankings (13).
时间序列聚类有着广泛的应用，但往往受到数据质量和现有方法固有局限性的限制。与图像等高维结构化数据相比，时间序列的低维特征包含的信息较少，并且内生噪声很容易掩盖重要模式。在处理海量时间序列数据时，现有的聚类方法通常侧重于挖掘序列之间的关联。然而，在存在噪声和信息稀缺的情况下，仅依靠成对关联分析很难获得理想的聚类结果。为了解决这些问题，我们提出了一个名为Patch Graph Mamba（PG-Mamba）的框架。首次通过将时间序列划分为多个片段并构建时空图（STG）来探索单个序列的时空模式。在这个图中，这些片段作为节点，由空间和时间边连接。通过利用Mamba驱动的长程依赖学习和解耦的时空图注意力机制，我们的框架同时捕捉时间动态和空间关系，从而能够从时间序列中有效提取关键信息。此外，引入了时空邻接矩阵重建损失，以减轻聚类损失引起的特征空间扰动。实验结果表明，PG-Mamba优于现有方法，为时间序列聚类任务提供了新的见解。在UCR时间序列存档的33个数据集中，PG-Mamba的平均排名最高，为3.606，并且获得第一名的次数最多（13次）。
