英文标题：
《Stock Forecasting using M-Band Wavelet-Based SVR and RNN-LSTMs Models》
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作者：
Hieu Quang Nguyen, Abdul Hasib Rahimyar, Xiaodi Wang
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最新提交年份：
2019
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英文摘要：
  The task of predicting future stock values has always been one that is heavily desired albeit very difficult. This difficulty arises from stocks with non-stationary behavior, and without any explicit form. Hence, predictions are best made through analysis of financial stock data. To handle big data sets, current convention involves the use of the Moving Average. However, by utilizing the Wavelet Transform in place of the Moving Average to denoise stock signals, financial data can be smoothened and more accurately broken down. This newly transformed, denoised, and more stable stock data can be followed up by non-parametric statistical methods, such as Support Vector Regression (SVR) and Recurrent Neural Network (RNN) based Long Short-Term Memory (LSTM) networks to predict future stock prices. Through the implementation of these methods, one is left with a more accurate stock forecast, and in turn, increased profits.
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中文摘要：
预测未来股票价值的任务一直是人们非常渴望的任务，尽管这非常困难。这种困难源于具有非平稳行为且没有任何明确形式的股票。因此，最好通过分析金融股票数据进行预测。为了处理大数据集，目前的惯例是使用移动平均值。然而，通过使用小波变换代替移动平均对股票信号进行去噪，可以平滑金融数据并更准确地分解。这种新转换、去噪和更稳定的股票数据可以采用非参数统计方法，如支持向量回归（SVR）和基于递归神经网络（RNN）的长-短期记忆（LSTM）网络来预测未来的股票价格。通过这些方法的实施，可以对库存进行更准确的预测，进而增加利润。
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分类信息：

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：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：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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