英文标题：
《Financial series prediction using Attention LSTM》
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作者：
Sangyeon Kim, Myungjoo Kang
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最新提交年份：
2019
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英文摘要：
  Financial time series prediction, especially with machine learning techniques, is an extensive field of study. In recent times, deep learning methods (especially time series analysis) have performed outstandingly for various industrial problems, with better prediction than machine learning methods. Moreover, many researchers have used deep learning methods to predict financial time series with various models in recent years. In this paper, we will compare various deep learning models, such as multilayer perceptron (MLP), one-dimensional convolutional neural networks (1D CNN), stacked long short-term memory (stacked LSTM), attention networks, and weighted attention networks for financial time series prediction. In particular, attention LSTM is not only used for prediction, but also for visualizing intermediate outputs to analyze the reason of prediction; therefore, we will show an example for understanding the model prediction intuitively with attention vectors. In addition, we focus on time and factors, which lead to an easy understanding of why certain trends are predicted when accessing a given time series table. We also modify the loss functions of the attention models with weighted categorical cross entropy; our proposed model produces a 0.76 hit ratio, which is superior to those of other methods for predicting the trends of the KOSPI 200.
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中文摘要：
金融时间序列预测，尤其是机器学习技术，是一个广泛的研究领域。近年来，深度学习方法（尤其是时间序列分析）在解决各种工业问题方面表现突出，其预测能力优于机器学习方法。此外，近年来，许多研究人员利用深度学习方法，利用各种模型对金融时间序列进行预测。在本文中，我们将比较各种深度学习模型，如多层感知器（MLP）、一维卷积神经网络（1D CNN）、堆叠式长短期记忆（堆叠式LSTM）、注意网络和用于金融时间序列预测的加权注意网络。特别是，注意LSTM不仅用于预测，还用于可视化中间输出，以分析预测的原因；因此，我们将展示一个示例，以直观地理解带有注意向量的模型预测。此外，我们将重点放在时间和因素上，这使得我们很容易理解为什么在访问给定的时间序列表时会预测某些趋势。我们还用加权分类交叉熵修正了注意模型的损失函数；我们提出的模型的命中率为0.76，优于其他预测KOSPI 200趋势的方法。
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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：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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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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