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2022 | OriginalPaper | Chapter

Telecommunication Stocks Prediction Using Long Short-Term Memory Model Neural Network

Authors : Nandini Jhanwar, Pratham Goel, Hemraj Lamkuche

Published in: Computing, Communication and Learning

Publisher: Springer Nature Switzerland

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Abstract

This study looks at how LSTM networks can be used to predict destiny stock fee patterns primarily based on fee history and technical evaluation. To achieve this, a methodology was developed, several tests were conducted, and the results were evaluated against a number of measures to see if this kind of algorithm outperforms other machine learning techniques. A major current trend in scientific study is machine learning, which involves teaching computers to perform tasks that would require human intelligence. This study uses Long-Short Term Memory Model, to develop a model that predicts future stock market values. The main goal of this paper is to evaluate the predictive accuracy of the machine learning algorithm and the extent to which epochs can improve our model.

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Title: Telecommunication Stocks Prediction Using Long Short-Term Memory Model Neural Network
Authors: Nandini Jhanwar
Pratham Goel
Hemraj Lamkuche
Publisher: Springer Nature Switzerland
Book: Computing, Communication and Learning
Print ISBN: 978-3-031-21749-4

Electronic ISBN: 978-3-031-21750-0

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-031-21750-0_26

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