论文标题
基于DCNN的库存预测模型
A Stock Prediction Model Based on DCNN
论文作者
论文摘要
股票价格的预测一直是一个具有挑战性的问题，因为它的波动性可能会受到许多因素，例如国家政策，公司财务报告，行业绩效和投资者的情绪等。在本文中，我们提出了一个基于Deep CNN和蜡烛图表的预测模型，处理了连续的时间库存信息。根据不同的信息丰富度，预测时间间隔和分类方法，原始数据被分为多个类别，为CNN的培训集。此外，卷积神经网络用于预测股票市场并分析不同分类方法下的准确性差异。 结果表明，当预测时间间隔为20天时，该方法具有最佳性能。此外，添加了移动平均收敛差异和三种移动平均值作为输入。该方法可以准确地预测美国NDAQ交换的股票趋势，为92.2％。同时，本文区分了三种常规分类方法，以提供未来研究的指导。
The prediction of a stock price has always been a challenging issue, as its volatility can be affected by many factors such as national policies, company financial reports, industry performance, and investor sentiment etc.. In this paper, we present a prediction model based on deep CNN and the candle charts, the continuous time stock information is processed. According to different information richness, prediction time interval and classification method, the original data is divided into multiple categories as the training set of CNN. In addition, the convolutional neural network is used to predict the stock market and analyze the difference in accuracy under different classification methods. The results show that the method has the best performance when the forecast time interval is 20 days. Moreover, the Moving Average Convergence Divergence and three kinds of moving average are added as input. This method can accurately predict the stock trend of the US NDAQ exchange for 92.2%. Meanwhile, this article distinguishes three conventional classification methods to provide guidance for future research.
