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Stock Market Prediction Using Recurrent Neural Network and Long Short-Term Memory
Bhoite S., Ansari G., , Thatte S., Magar V., Gandhi K.
Published in Springer Science and Business Media Deutschland GmbH
2023
Volume: 520
   
Pages: 635 - 643
Abstract
The aim this paper is to make the trader’s life easy with all kinds of exploratory data analysis and to bring the forecast with the deep learning model. We have used a different kind of analysis to come to a point on which we have built a model completely based on long short-term memory and recurrent neural network. We have also performed different types of graphs to explain the trend of the company user searches. The trend is a line graph depicting years and months based on user selection. Since forecasting has highly emerged, there has been a lot of research on this topic. The stock market is always the scientist’s favourite category to show their skills. This paper attention on the analysis and prediction of stock values that do not take care of party-political tenures, and financial tension which disturbs the stock market. This model will assist a stockholder, distinct user or the general public to make protected investments. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About the journal
JournalLecture Notes in Networks and Systems
PublisherSpringer Science and Business Media Deutschland GmbH
ISSN23673370
Open AccessNo