This paper predicts the average stock price for five datasets by utilizing the historical stock price data ranging from April 2009 to February 2019. Autoregressive Integrated Moving Average(ARIMA) model is used to generate the baseline, while Long ShortTerm Memory(LSTM) networks is used to build the forecasting model for predicting the stock price. The efficiency of the two models is compared in terms of Mean Squared Error. The results show that the LSTM model predicts better than the ARIMA model with respect to time series forecasting. Additionally, Attention LSTM networks is employed to further study the improvement in accuracy of the stock price forecasting model.
类型: 国际会议
作者: Baleshwarsingh Joosery,G Deepa
来源: 2019 International Conference on Advanced Information Science and System (AISS 2019) 2019-11-15
年度: 2019
分类: 基础科学,信息科技,经济与管理科学
专业: 数学,自动化技术,金融,证券,投资
单位: Department of Computer Science & Engineering Manipal Institute of Technology, Manipal Academy of Higher Education
分类号: F832.51;TP18;O211.61
DOI: 10.26914/c.cnkihy.2019.078777
页码: 197-202
总页数: 6
文件大小: 268k
本文来源: https://www.lunwen90.cn/article/2bcaea52e6302d74c2dd2aa3.html