The application of Long Short-Term Memory algorithm in American multinational technology company stock prediction

Research Article
Open access

The application of Long Short-Term Memory algorithm in American multinational technology company stock prediction

Yufei Wang 1*
  • 1 Ramona Convent Secondary School    
  • *corresponding author ywang.23@ramonaconvent.org
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/13/20230719
ACE Vol.13
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-017-2
ISBN (Online): 978-1-83558-018-9

Abstract

The fast development in American multinational technology companies has attracted both professional and new investors to buy the stocks. However, the price of these companies are unstable and therefore hard to be predicted. The focus of this article is to use AI and deep learning algorithms to find a pattern of the stock price. Long Short-Term Memory Algorithm (LSTM) is the main algorithm used to predict the trend, and other methods including Autoregressive integrated moving average (ARIMA), Seasonal autoregressive integrated moving average (SARIMA), and prophet are also discussed in this piece.

Keywords:

big data, Long Short-Term Memory, Artificial Intelligence and deep-learning

Wang,Y. (2023). The application of Long Short-Term Memory algorithm in American multinational technology company stock prediction. Applied and Computational Engineering,13,131-133.
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References

[1]. Google Stock Data | Kaggle

[2]. Kumar G, Singh UP, Jain S. An adaptive particle swarm optimization-based hybrid long short-term memory model for stock price time series forecasting. Soft comput. 2022;26(22):12115-12135. doi: 10.1007/s00500-022-07451-8. Epub 2022 Aug 26. PMID: 36043118; PMCID: PMC9415266.

[3]. Huang Y, Gao Y, Gan Y, Ye M. A new financial data forecasting model using genetic algorithm and long short-term memory network. Neurocomputing. 2021;425:207–218.

[4]. Fischer T, Krauss C. Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res. 2018;270(2):654–669.

[5]. Alwee R, Shamsuddin SM, Sallehuddin R. Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators. ScientificWorldJournal. 2013 May 23;2013:951475. doi: 10.1155/2013/951475. PMID: 23766729; PMCID: PMC3677664.

[6]. Suradhaniwar S, Kar S, Durbha SS, Jagarlapudi A. Time Series Forecasting of Univariate Agrometeorological Data: A Comparative Performance Evaluation via One-Step and Multi-Step Ahead Forecasting Strategies. Sensors (Basel). 2021 Apr 1;21(7):2430. doi: 10.3390/s21072430. PMID: 33916026; PMCID: PMC8037998.

[7]. Crash Diagnosis and Price Rebound Prediction in NYSE Composite Index Based on Visibility Graph and Time-Evolving Stock Correlation Network

Cite this article

Wang,Y. (2023). The application of Long Short-Term Memory algorithm in American multinational technology company stock prediction. Applied and Computational Engineering,13,131-133.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-017-2(Print) / 978-1-83558-018-9(Online)
Editor：Roman Bauer, Marwan Omar, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.13
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Google Stock Data | Kaggle

[2]. Kumar G, Singh UP, Jain S. An adaptive particle swarm optimization-based hybrid long short-term memory model for stock price time series forecasting. Soft comput. 2022;26(22):12115-12135. doi: 10.1007/s00500-022-07451-8. Epub 2022 Aug 26. PMID: 36043118; PMCID: PMC9415266.

[3]. Huang Y, Gao Y, Gan Y, Ye M. A new financial data forecasting model using genetic algorithm and long short-term memory network. Neurocomputing. 2021;425:207–218.

[4]. Fischer T, Krauss C. Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res. 2018;270(2):654–669.

[5]. Alwee R, Shamsuddin SM, Sallehuddin R. Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators. ScientificWorldJournal. 2013 May 23;2013:951475. doi: 10.1155/2013/951475. PMID: 23766729; PMCID: PMC3677664.

[6]. Suradhaniwar S, Kar S, Durbha SS, Jagarlapudi A. Time Series Forecasting of Univariate Agrometeorological Data: A Comparative Performance Evaluation via One-Step and Multi-Step Ahead Forecasting Strategies. Sensors (Basel). 2021 Apr 1;21(7):2430. doi: 10.3390/s21072430. PMID: 33916026; PMCID: PMC8037998.

[7]. Crash Diagnosis and Price Rebound Prediction in NYSE Composite Index Based on Visibility Graph and Time-Evolving Stock Correlation Network

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