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.
翻译:我们通过深层学习技术设计了限制订单簿(LOB)数据多光速预测模型。与进行单一预测的标准结构不同,我们采用了带有序列到序列和注意机制的编码解码模型来生成预测路径。我们的方法在短预测视野中实现了与最先进的算法的类似性能。重要的是,在利用多光谱设置来生成长期预测时,这些方法优于远距预测。鉴于编码解码模型依赖于经常性神经层,它们通常受到缓慢的培训过程的影响。为了纠正这种情况,我们试验了由Greamcore公司生产的新型硬件,即所谓的智能处理器(UIPUs)。议会联盟专门设计了机器智能工作量,目的是加快计算过程。我们显示,在我们的设置中,与GPU公司培训模型相比,这种培训速度要快得多。