英文标题：
《Temporal Logistic Neural Bag-of-Features for Financial Time series
  Forecasting leveraging Limit Order Book Data》
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作者：
Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj,
  Alexandros Iosifidis
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最新提交年份：
2019
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英文摘要：
  Time series forecasting is a crucial component of many important applications, ranging from forecasting the stock markets to energy load prediction. The high-dimensionality, velocity and variety of the data collected in these applications pose significant and unique challenges that must be carefully addressed for each of them. In this work, a novel Temporal Logistic Neural Bag-of-Features approach, that can be used to tackle these challenges, is proposed. The proposed method can be effectively combined with deep neural networks, leading to powerful deep learning models for time series analysis. However, combining existing BoF formulations with deep feature extractors pose significant challenges: the distribution of the input features is not stationary, tuning the hyper-parameters of the model can be especially difficult and the normalizations involved in the BoF model can cause significant instabilities during the training process. The proposed method is capable of overcoming these limitations by a employing a novel adaptive scaling mechanism and replacing the classical Gaussian-based density estimation involved in the regular BoF model with a logistic kernel. The effectiveness of the proposed approach is demonstrated using extensive experiments on a large-scale financial time series dataset that consists of more than 4 million limit orders.
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中文摘要：
从股市预测到能源负荷预测，时间序列预测是许多重要应用的重要组成部分。在这些应用程序中收集的数据的高维性、速度和多样性带来了重大而独特的挑战，必须认真解决每个问题。在这项工作中，提出了一种新的时间逻辑神经特征袋方法，可以用来解决这些挑战。该方法可以有效地与深度神经网络相结合，为时间序列分析提供强大的深度学习模型。然而，将现有BoF公式与深度特征提取器相结合带来了重大挑战：输入特征的分布不是固定的，调整模型的超参数可能特别困难，并且BoF模型中涉及的规范化可能会在训练过程中造成显著的不稳定性。该方法采用了一种新的自适应缩放机制，并用logistic核取代了常规转炉模型中基于高斯的密度估计，从而克服了这些局限性。在一个由400多万个限额订单组成的大规模金融时间序列数据集上进行了大量实验，证明了该方法的有效性。
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分类信息：

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。
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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
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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