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We design multi-horizon forecasting models for limit order book (LOB) data byusing deep learning techniques. Unlike standard structures where a singleprediction is made, we adopt encoder-decoder models with sequence-to-sequenceand Attention mechanisms to generate a forecasting path. Our methods achievecomparable performance to state-of-art algorithms at short prediction horizons.Importantly, they outperform when generating predictions over long horizons byleveraging the multi-horizon setup. Given that encoder-decoder models rely onrecurrent neural layers, they generally suffer from slow training processes. Toremedy this, we experiment with utilising novel hardware, so-called IntelligentProcessing Units (IPUs) produced by Graphcore. IPUs are specifically designedfor machine intelligence workload with the aim to speed up the computationprocess. We show that in our setup this leads to significantly faster trainingtimes when compared to training models with GPUs.

Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units