英文标题：
《Temporal Attention augmented Bilinear Network for Financial Time-Series
  Data Analysis》
---
作者：
Dat Thanh Tran, Alexandros Iosifidis, Juho Kanniainen, Moncef Gabbouj
---
最新提交年份：
2017
---
英文摘要：
  Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market. In the High-Frequency Trading (HFT), forecasting for trading purposes is even a more challenging task since an automated inference system is required to be both accurate and fast. In this paper, we propose a neural network layer architecture that incorporates the idea of bilinear projection as well as an attention mechanism that enables the layer to detect and focus on crucial temporal information. The resulting network is highly interpretable, given its ability to highlight the importance and contribution of each temporal instance, thus allowing further analysis on the time instances of interest. Our experiments in a large-scale Limit Order Book (LOB) dataset show that a two-hidden-layer network utilizing our proposed layer outperforms by a large margin all existing state-of-the-art results coming from much deeper architectures while requiring far fewer computations.
---
中文摘要：
由于市场固有的噪声和随机性，金融时间序列预测长期以来一直是一个具有挑战性的问题。在高频交易（HFT）中，出于交易目的进行预测是一项更具挑战性的任务，因为自动推理系统要求既准确又快速。在本文中，我们提出了一种神经网络层结构，该结构融合了双线性投影的思想以及一种注意机制，使该层能够检测和关注关键的时间信息。由此产生的网络具有高度的可解释性，因为它能够突出每个时间实例的重要性和贡献，从而允许对感兴趣的时间实例进行进一步分析。我们在一个大规模限制订单（LOB）数据集上的实验表明，利用我们提出的层的两个隐藏层网络在很大程度上优于所有来自更深层体系结构的现有最先进的结果，同时需要更少的计算。
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computational Engineering, Finance, and Science        计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
--
一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
--
一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
--

---
PDF下载：
-->