Time series clustering is usually an essential unsupervised task in cases when category information is not available and has a wide range of applications. However, existing time series clustering methods usually either ignore temporal dynamics of time series or isolate the feature extraction from clustering tasks without considering the interaction between them. In this article, a time series clustering framework named self-supervised time series clustering network (STCN) is proposed to optimize the feature extraction and clustering simultaneously. In the feature extraction module, a recurrent neural network (RNN) conducts a one-step time series prediction that acts as the reconstruction of the input data, capturing the temporal dynamics and maintaining the local structures of the time series. The parameters of the output layer of the RNN are regarded as model-based dynamic features and then fed into a self-supervised clustering module to obtain the predicted labels. To bridge the gap between these two modules, we employ spectral analysis to constrain the similar features to have the same pseudoclass labels and align the predicted labels with pseudolabels as well. STCN is trained by iteratively updating the model parameters and the pseudoclass labels. Experiments conducted on extensive time series data sets show that STCN has state-of-the-art performance, and the visualization analysis also demonstrates the effectiveness of the proposed model.

中文翻译：

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使用基于模型的动力学进行自我监督的时间序列聚类

在类别信息不可用且具有广泛应用的情况下，时间序列聚类通常是必不可少的无监督任务。然而，现有的时间序列聚类方法通常要么忽略时间序列的时间动态，要么将特征提取与聚类任务隔离开来，而不考虑它们之间的相互作用。在本文中，提出了一种名为自监督时间序列聚类网络（STCN）的时间序列聚类框架，以同时优化特征提取和聚类。在特征提取模块中，循环神经网络 (RNN) 进行一步时间序列预测，作为输入数据的重建，捕获时间动态并保持时间序列的局部结构。RNN 输出层的参数被视为基于模型的动态特征，然后馈入自监督聚类模块以获得预测标签。为了弥合这两个模块之间的差距，我们采用频谱分析来限制相似特征具有相同的伪类标签，并将预测标签与伪标签对齐。STCN 通过迭代更新模型参数和伪类标签来训练。在大量时间序列数据集上进行的实验表明 STCN 具有最先进的性能，可视化分析也证明了所提出模型的有效性。我们采用频谱分析来限制相似特征具有相同的伪类标签，并将预测标签与伪标签对齐。STCN 通过迭代更新模型参数和伪类标签来训练。在大量时间序列数据集上进行的实验表明 STCN 具有最先进的性能，可视化分析也证明了所提出模型的有效性。我们采用频谱分析来限制相似特征具有相同的伪类标签，并将预测标签与伪标签对齐。STCN 通过迭代更新模型参数和伪类标签来训练。在大量时间序列数据集上进行的实验表明 STCN 具有最先进的性能，可视化分析也证明了所提出模型的有效性。