In this paper, we consider the task of clustering a set of individual time series while modeling each cluster, that is, model-based time series clustering. The task requires a parametric model with sufficient flexibility to describe the dynamics in various time series. To address this problem, we propose a novel model-based time series clustering method with mixtures of linear Gaussian state space models, which have high flexibility. The proposed method uses a new expectation-maximization algorithm for the mixture model to estimate the model parameters, and determines the number of clusters using the Bayesian information criterion. Experiments on a simulated dataset demonstrate the effectiveness of the method in clustering, parameter estimation, and model selection. The method is applied to real datasets commonly used to evaluate time series clustering methods. Results showed that the proposed method produces clustering results that are as accurate or more accurate than those obtained using previous methods.

翻译：在本文中,我们考虑在对每个组群进行建模时,将一组单个的时间序列分组,即以模型为基础的时间序列分组的任务。任务要求有一个具有足够灵活性的参数模型来描述不同时间序列的动态。为了解决这个问题,我们建议采用一种新型基于模型的时间序列分组方法,结合具有高度灵活性的线性高斯状态空间模型混合物。拟议方法对混合物模型使用一种新的预期-最大化算法来估计模型参数,并用巴伊西亚信息标准确定组群的数目。模拟数据集实验显示集、参数估计和模型选择方法的有效性。该方法适用于通常用于评价时间序列组合方法的实际数据集。结果显示,拟议方法产生的集束结果与以前使用的方法相比准确或更准确。