  Financial market analysis, especially the prediction of movements of stock
prices, is a challenging problem. The nature of financial time-series data,
being non-stationary and nonlinear, is the main cause of these challenges. Deep
learning models have led to significant performance improvements in many
problems coming from different domains, including prediction problems of
financial time-series data. Although the prediction performance is the main
goal of such models, dealing with ultra high-frequency data sets restrictions
in terms of the number of model parameters and its inference speed. The
Temporal Attention-Augmented Bilinear network was recently proposed as an
efficient and high-performing model for Limit Order Book time-series
forecasting. In this paper, we propose a low-rank tensor approximation of the
model to further reduce the number of trainable parameters and increase its
speed.
