A Model-Based Multivariate Time Series Clustering Algorithm

Given a set of multivariate time series, the problem of clustering such data is concerned with the discovering of inherent groupings of the data according to how similar or dissimilar the time series are to each other. Existing time series clustering algorithms can divide into three types, raw-based, featurebased and model-based. In this paper, a model-based multivariate time series clustering algorithm is proposed and its tasks in several steps: (i)data transformation, (ii)discovering time series temporal patterns using confidence value to represent the relationship between different variables, (iii) clustering of multivariate time series based on the degree of patterns discovering in (ii). For evaluate performance of proposed algorithm, the proposed algorithm is tested with both synthetic data and real data. The result shows that it can be promising algorithm for multivariate time series clustering.