Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space Models
This addresses the problem of time series clustering for data analysis, but it appears incremental as it builds on existing model-based approaches with a new EM algorithm.
The paper tackles the problem of clustering time series by proposing a model-based method using mixtures of linear Gaussian state space models, with results showing it produces clustering as accurate or more accurate than previous methods on real datasets.
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.