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

页码：444 / 453

页数：10

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