  We design multi-horizon forecasting models for limit order book (LOB) data by
using deep learning techniques. Unlike standard structures where a single
prediction is made, we adopt encoder-decoder models with sequence-to-sequence
and Attention mechanisms to generate a forecasting path. Our methods achieve
comparable performance to state-of-art algorithms at short prediction horizons.
Importantly, they outperform when generating predictions over long horizons by
leveraging the multi-horizon setup. Given that encoder-decoder models rely on
recurrent neural layers, they generally suffer from slow training processes. To
remedy this, we experiment with utilising novel hardware, so-called Intelligent
Processing Units (IPUs) produced by Graphcore. IPUs are specifically designed
for machine intelligence workload with the aim to speed up the computation
process. We show that in our setup this leads to significantly faster training
times when compared to training models with GPUs.
