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Temporal logistic neural Bag-of-Features for financial time series forecasting leveraging limit order book data
Pattern Recognition Letters ( IF 5.1 ) Pub Date : 2020-06-09 , DOI: 10.1016/j.patrec.2020.06.006
Nikolaos Passalis , Anastasios Tefas , Juho Kanniainen , Moncef Gabbouj , Alexandros Iosifidis

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 many of 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 limit order book dataset that consists of more than 4 million limit orders.



中文翻译:

时间后勤神经功能袋,利用限价订单数据进行财务时间序列预测

时间序列预测是许多重要应用程序中至关重要的组成部分,从预测股市到预测能源负荷。在许多这些应用程序中收集的数据的高维度,速度和多样性构成了重大而独特的挑战,必须为每个应用程序认真解决这些挑战。在这项工作中,提出了一种新颖的时态逻辑神经特征袋方法,可以用来应对这些挑战。所提出的方法可以有效地与深度神经网络相结合,从而为时间序列分析提供强大的深度学习模型。但是,将现有的BoF公式与深层特征提取器结合起来会带来巨大挑战: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引起严重的不稳定性。所提出的方法能够通过采用新颖的自适应缩放机制并用对数核替换常规BoF模型中涉及的基于经典高斯的密度估计来克服这些限制。通过对包含超过400万个限价订单的大规模限价订单数据集进行广泛的实验,证明了该方法的有效性。所提出的方法能够通过采用新颖的自适应缩放机制并用对数核替换常规BoF模型中涉及的基于经典高斯的密度估计来克服这些限制。通过对包含超过400万个限价订单的大规模限价订单数据集进行广泛的实验,证明了该方法的有效性。所提出的方法能够通过采用新颖的自适应缩放机制并用逻辑内核替换常规BoF模型中涉及的基于经典高斯的密度估计来克服这些限制。通过对包含超过400万个限价订单的大规模限价订单数据集进行广泛的实验,证明了该方法的有效性。

更新日期:2020-06-09
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