Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space Models

• URL: http://arxiv.org/abs/2208.11907v1
• Date: Thu, 25 Aug 2022 07:41:23 GMT
• Title: Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space Models
• Authors: Ryohei Umatani, Takashi Imai, Kaoru Kawamoto, Shutaro Kunimasa
• Abstract summary: We propose a novel model-based time series clustering method with mixtures of linear Gaussian state space models. The proposed method uses a new expectation-maximization algorithm for the mixture model to estimate the model parameters. Experiments on a simulated dataset demonstrate the effectiveness of the method in clustering, parameter estimation, and model selection.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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