Financial time-series analysis and forecasting have been extensively studied over the past decades, yet still remain as a very challenging research topic. Since the financial market is inherently noisy and stochastic, a majority of financial time-series of interests are non-stationary, and often obtained from different modalities. This property presents great challenges and can significantly affect the performance of the subsequent analysis/forecasting steps. Recently, the Temporal Attention augmented Bilinear Layer (TABL) has shown great performances in tackling financial forecasting problems. In this paper, by taking into account the nature of bilinear projections in TABL networks, we propose Bilinear Normalization (BiN), a simple, yet efficient normalization layer to be incorporated into TABL networks to tackle potential problems posed by non-stationarity and multimodalities in the input series. Our experiments using a large scale Limit Order Book (LOB) consisting of more than 4 million order events show that BiN-TABL outperforms TABL networks using other state-of-the-arts normalization schemes by a large margin.

中文翻译：

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高频金融时间序列中双线性结构的数据规范化

在过去的几十年里，金融时间序列分析和预测得到了广泛的研究，但仍然是一个非常具有挑战性的研究课题。由于金融市场本质上是嘈杂和随机的，因此大多数金融时间序列的兴趣是非平稳的，并且经常从不同的模式中获得。此属性提出了巨大的挑战，并且会显着影响后续分析/预测步骤的性能。最近，时间注意力增强双线性层（TABL）在解决金融预测问题方面表现出色。在本文中，通过考虑 TABL 网络中双线性投影的性质，我们提出了双线性归一化 (BiN)，一种简单的、然而，高效的归一化层将被纳入 TABL 网络，以解决输入序列中非平稳性和多模态带来的潜在问题。我们使用由超过 400 万个订单事件组成的大规模限价订单 (LOB) 的实验表明，BiN-TABL 大大优于使用其他最先进标准化方案的 TABL 网络。