The Prediction Stock Market Price Using LSTM

被引：0
|
作者：
Barik, Rhada [1 ]
Baina, Amine [1 ]
Bellafkih, Mostafa [1 ]
机构：
[1] Natl Inst Posts & Telecommun, Rabat, Morocco
来源：
EMERGING TRENDS IN INTELLIGENT SYSTEMS & NETWORK SECURITY | 2023年 / 147卷
关键词：
Time series; Stock market prediction; Long Short Term Memory LSTM;
D O I：
10.1007/978-3-031-15191-0_42
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we will focus on the applicability of recurrent neural networks, particularly the Long Short Term Memory networks, in predicting the NASDAQ and the S&P 500 stock market prices were investigated. Daily stock exchange rates of NASDAQ and S&P 500 from January 4, 2010, to January 30, 2020, are used to construct a robust model. By building a model with various configurations of LSTM can be tested and compared. We used two evaluation measures, the coefficient of determination R-2 as well as the "Root Mean Squared Error" RMSE, in order to judge the relevance of the results.
引用
收藏
页码：444 / 453
页数：10
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