  Financial time-series forecasting has long been a challenging problem because
of the inherently noisy and stochastic nature of the market. In the
High-Frequency Trading (HFT), forecasting for trading purposes is even a more
challenging task since an automated inference system is required to be both
accurate and fast. In this paper, we propose a neural network layer
architecture that incorporates the idea of bilinear projection as well as an
attention mechanism that enables the layer to detect and focus on crucial
temporal information. The resulting network is highly interpretable, given its
ability to highlight the importance and contribution of each temporal instance,
thus allowing further analysis on the time instances of interest. Our
experiments in a large-scale Limit Order Book (LOB) dataset show that a
two-hidden-layer network utilizing our proposed layer outperforms by a large
margin all existing state-of-the-art results coming from much deeper
architectures while requiring far fewer computations.
