Stock Price Prediction Using LSTM and GRU

Authors

  • Maladi Sri Raghavendra Uday Kiran   B. Tech, Computer Sciences, Vellore Institute of Technology, Andhra Pradesh, India

Keywords:

Long Bad Temper, Graphical Processing Circuit, Graphical Processing Circuit

Abstract

As that stock market is a very complex mathematical movement system with myriad factors which influences its fluctuation law, forecasting the economy is a risky task. Many finding suggest that Neural Network algorithms are well suited for such time series models and as often produce excellent results. Based on the analysis models, we revealed a Laplacian GRULSTM pattern recognition prediction models and applied it in the long calculation of all the two stocks' closing prices. In stock time series prediction, the simulations results reveal that in out proposed model gives the political and LSTM network models.

References

  1. Yung-Keun Kwon and Byung-Ro Moon (2007). Stock Tracking Using a Multi Neurogenetic Approach. Combine Data on Neural Networks is an Ee Nn Congress journal. 18. 851-64. 10.1109/TNN.2007.891629.
  2. Singh, R. & Srivastava, S. Multimed Tools Appl (2017) 76: 18569 9
  3. Predicting employee stock price index behavior using Economic growth Deterministic Knowledge Worker ” or “ Computer Vision, Svms with Entries, Segment 42, Issue 1, 2015, Pages 259-268.
  4. George S. Atsalakis and Kimon P. Worth, ’s strong stock market forecast models - Part II: Model - based tools, Svms with Extensions, Output 36, Issue 3, Part 2, 2009, pp. 5932-5941.
  5. Predicting prices of the two x - beam fusion of learning methods, Expert Systems with Applications, Volume 42, Issue 4, 2015, Pages 2162-2172.
  6. Lamartine Almeida Teixeira, Adriano Lorena Inácio de Oliveira, Expert Systems with Applications is a strategy for computerized stock trading that combines technical analysis and closest neighbor categorization, Volume 37, Issue 10, 2010, Pages 6885-6890
  7. Chen, Kai, Zhou, Yi, and Fangyan Dai (2015). A case study of the Chinese stock market using a Hmm model foranalyzingstockprice.10.1109/BigData.2015.7364089. 2823-2824.
  8. Erkam Guresen, Gulgun Kayakutlu, and Tugrul U. Meindl, Using anfis methods to analyze market returns domains, Expert Systems with Applications, Volume 38, Issue 8, 2011, Pages 10389-10397.
  9. Wei Shen, Xiaopen Guo, Chao Wu, Desheng Wu, Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm, Knowledge-Based Systems, Volume 24, Issue 3, 2011, Pages 378-385
  10. Emad W. Saad Danil V. Prokhorov Donald C. Wunsch "Comparative study of stock trend prediction using time delay recurrent and probabilistic neural networks" Neural Networks IEEE Transactions on vol. 9 no. 6 pp. 1456-1470 1998.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-02-28

Issue

Volume 9, Issue 1, January-February-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Maladi Sri Raghavendra Uday Kiran , " Stock Price Prediction Using LSTM and GRU" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 1, pp.200-205, January-February-2023.