  Real-time prediction plays a vital role in various control systems, such as
traffic congestion control and wireless channel resource allocation. In these
scenarios, the predictor usually needs to track the evolution of the latent
statistical patterns in the modern high-dimensional streaming time series
continuously and quickly, which presents new challenges for traditional
prediction methods. This paper proposes a novel algorithm based on tensor
factorization to predict streaming tensor time series online. The proposed
algorithm updates the predictor in a low-complexity online manner to adapt to
the time-evolving data. Additionally, an automatically adaptive version of the
algorithm is presented to mitigate the negative impact of stale data.
Simulation results demonstrate that our proposed methods achieve prediction
accuracy similar to that of conventional offline tensor prediction methods,
while being much faster than them during long-term online prediction.
Therefore, our proposed algorithm provides an effective and efficient solution
for the online prediction of streaming tensor time series.
