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 a real dataset for which previously proposed time series clustering methods exhibited low accuracy. Results showed that our method produces more accurate clustering results than those obtained using the 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,配有具有高度灵活性的线性高斯国家空间模型混合物。拟议方法对混合物模型采用一种新的预期-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。该方法应用到一个真实的数据集,而以前提议的时间序列组合方法的精确度较低。结果显示,我们的方法比以前使用的方法产生更准确的组合结果。