  In this paper, we study a Bayesian tensor train (TT) decomposition method to
recover streaming data by approximating the latent structure in high-order
streaming data. Drawing on the streaming variational Bayes method, we introduce
the TT format into Bayesian tensor decomposition methods for streaming data,
and formulate posteriors of TT cores. Thanks to the Bayesian framework of the
TT format, the proposed algorithm (SPTT) excels in recovering streaming data
with high-order, incomplete, and noisy properties. The experiments in synthetic
and real-world datasets show the accuracy of our method compared to
state-of-the-art Bayesian tensor decomposition methods for streaming data.
