  Clustering algorithms have wide applications and play an important role in
data analysis fields including time series data analysis. However, in time
series analysis, most of the algorithms used signal shape features or the
initial value of hidden variable of a neural network. Little has been discussed
on the methods based on the generative model of the time series. In this paper,
we propose a new clustering algorithm focusing on the generative process of the
signal with a recurrent neural network and the variational Bayes method. Our
experiments show that the proposed algorithm not only has a robustness against
for phase shift, amplitude and signal length variations but also provide a
flexible clustering based on the property of the variational Bayes method.
