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

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Computing, Communication and Learning (CoCoLe 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1729))

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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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Author information

Authors and Affiliations

  1. Symbiosis University of Applied Sciences, Indore, India

    Nandini Jhanwar, Pratham Goel & Hemraj Lamkuche

Authors
  1. Nandini Jhanwar
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  2. Pratham Goel
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  3. Hemraj Lamkuche
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Corresponding author

Correspondence to Nandini Jhanwar .

Editor information

Editors and Affiliations

  1. National Institute of Technology, Warangal, India

    Sanjaya Kumar Panda

  2. National Institute of Technology, Warangal, India

    Rashmi Ranjan Rout

  3. National Institute of Technology, Warangal, India

    Ravi Chandra Sadam

  4. National Institute of Technology, Warangal, India

    Bala Venkata Subramaanyam Rayanoothala

  5. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  6. University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

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Jhanwar, N., Goel, P., Lamkuche, H. (2022). Telecommunication Stocks Prediction Using Long Short-Term Memory Model Neural Network. In: Panda, S.K., Rout, R.R., Sadam, R.C., Rayanoothala, B.V.S., Li, KC., Buyya, R. (eds) Computing, Communication and Learning. CoCoLe 2022. Communications in Computer and Information Science, vol 1729. Springer, Cham. https://doi.org/10.1007/978-3-031-21750-0_26

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  • DOI: https://doi.org/10.1007/978-3-031-21750-0_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21749-4

  • Online ISBN: 978-3-031-21750-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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