论文标题
高频财务时间序列中双线性结构的数据归一化
Data Normalization for Bilinear Structures in High-Frequency Financial Time-series
论文作者
论文摘要
在过去的几十年中，对财务时间序列分析和预测进行了广泛的研究，但仍然是一个非常具有挑战性的研究主题。由于金融市场本质上是嘈杂的和随机的，因此大多数利益的财务时间序列都是非统计的，并且通常是从不同方式获得的。该属性提出了巨大的挑战，可以显着影响后续分析/预测步骤的性能。最近，暂时的关注增强双线性层（TABL）在解决财务预测问题方面表现出色。在本文中，考虑到表格网络中双线性投影的性质，我们提出了双线性标准化（BIN），这是一个简单而有效的归一化层，可以将其纳入表网络中，以解决输入系列中非平稳性和多模态构成的潜在问题。我们使用由超过400万个订单事件组成的大规模限制订单簿（LOB）进行的实验表明，Bin-Tabl的表现优于表格网络，使用其他最先进的标准化方案较大。
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.
