Incomplete multivariate time series (MTS) clustering is a prevalent research topic in time series analysis, aimed at partitioning MTS containing missing data into distinct clusters. Contrastive learning-based multi-view clustering methods are a promising approach to address this issue. However, existing methods are typically not designed for time series. Specifically, most of these methods struggle to capture the inherent properties of time series, and are susceptible to losing their interdimensional correlations, thereby compromising data integrity. Furthermore, they commonly utilize data augmentation techniques to generate sample pairs for contrastive learning. These existing data augmentation techniques are not suitable for time series, and introduce uncertainty factors, which can diminish the representation learning capacity of contrastive learning. To address the challenges, we propose a contrastive learning-based multi-view clustering method for incomplete multivariate time series (MVCIMTS). In this method, each variable within the MTS is treated as a separate view, enabling a multi-view learning approach. To better leverage the intrinsic information of time series, we utilize a GRU-based model architecture that integrates imputation and clustering within a unified deep learning framework. In this way, missing views can be effectively inferred, and representations suitable for clustering can be learned, thereby enhancing the clustering performance for incomplete time series. Furthermore, we introduce an innovative contrastive learning approach specifically tailored for MTS, which ensures that the exploration of common semantics and clustering consistency across views remains unaffected by uncertainty factors. It assumes that each time series variable within the same sample has similar representations, thereby taking into account the correlation between variables and enhancing the quality of the representations. To the best of our knowledge, this is the first attempt at applying contrastive learning-based multi-view deep clustering to incomplete MTS. We conduct extensive comparative experiments with five multi-view clustering methods and two time series clustering methods on seven benchmark datasets. The results demonstrate that our proposed method is superior to other state-of-the-art methods.

中文翻译：

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基于对比学习的多视图聚类，用于不完全多变量时间序列


不完全多变量时间序列 （MTS） 聚类是时间序列分析中一个普遍的研究主题，旨在将包含缺失数据的 MTS 划分为不同的聚类。基于对比学习的多视图聚类方法是解决这个问题的一种很有前途的方法。但是，现有方法通常不是为时间序列设计的。具体来说，这些方法中的大多数都难以捕获时间序列的固有属性，并且容易丢失其维度间相关性，从而损害数据完整性。此外，他们通常利用数据增强技术来生成样本对以进行对比学习。这些现有的数据增强技术不适合时间序列，并引入了不确定性因素，这可能会降低对比学习的表征学习能力。为了应对这些挑战，我们提出了一种基于对比学习的多视图聚类方法，用于不完全多变量时间序列 （MVCIMTS）。在这种方法中，MTS 中的每个变量都被视为一个单独的视图，从而支持多视图学习方法。为了更好地利用时间序列的内在信息，我们利用了基于 GRU 的模型架构，该架构将插补和聚类集成到一个统一的深度学习框架中。通过这种方式，可以有效地推断出缺失的视图，并可以学习适合聚类的表示，从而提高不完整时间序列的聚类性能。此外，我们引入了一种专为 MTS 量身定制的创新对比学习方法，确保对常见语义的探索和跨视图的聚类一致性不受不确定性因素的影响。 它假设同一样本中的每个时间序列变量都具有相似的表示形式，从而考虑变量之间的相关性并提高表示的质量。据我们所知，这是将基于对比学习的多视图深度聚类应用于不完整 MTS 的首次尝试。我们在 7 个基准数据集上使用 5 种多视图聚类方法和 2 种时间序列聚类方法进行了广泛的比较实验。结果表明，我们提出的方法优于其他最先进的方法。