The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Stock Price prediction using LSTM and SVR
Abstract
Stock price movement is non-linear and complex. Several research works have been carried out to predict stock prices. Traditional approaches such as Linear Regression and Support Vector Regression were used but accuracy was not adequate. Researchers have tried to improve stock price prediction using ARIMA. Due to very high variations in stock prices, deep learning techniques are applied due to its proven accuracy in various analytics fields. Artificial Neural Network was deployed to predict stock prices but as stock prices are time-series based, recurrent neural network was applied to further improve prediction accuracy. In RNN, there is limitation of not able to store high dependencies and also vanishing gradient descent issue exists. Therefore, data scientists and analysts applied LSTM to predict stock price movement. In this paper, LSTM is compared with SVR using various stock index data such as S& P 500, NYSE, NSE, BSE, NASDAQ and Dow Jones industrial Average for experiment analysis. Experiment analysis proves that LSTM provides better accuracy as compared to SVR.
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LSTM has greater accuracy than SVR when compared using several stock indices.
“As a result, employing several stock indices, LSTM was implemented and compared to SVR, demonstrating LSTM's greater accuracy.”
From Intelligent Decision Making for Commodities Price Prediction: Opportunities, Challenges and Future Avenues (2025) · cites this paper as Bathla, 2020
Citations
Cites
Temporal Relational Ranking for Stock Prediction
Feng, F., He, X., Wang, X., Luo, C., Liu, Y., & Chua, T.-S. (2019) · ACM Transactions on Information Systems · doi:10.1145/3309547 · Cited by 553
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Saeed, N., Shafi, I., Pervez, S., Thompson, E. B., Castilla, A. K., Samad, M. A., & Ashraf, I. (2025) · Computational Economics · doi:10.1007/s10614-024-10837-5 · Cited by 8
Metadata
- Authors
- Bathla, G.
- Affiliations
- University of Petroleum and Energy Studies (IN)
- Corresponding author
- Bathla, Gourav
- ORCID
- Bathla, Gourav
- Venue
- 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)
- Publisher
- IEEE
- Published
- 2020-11-06
- Pages
- 211-214
- OpenAlex
- W3123007418
- Cited by
- 139
- References
- 1
- Citations by year
- 2026: 14 · 2025: 21 · 2024: 47 · 2023: 30 · 2022: 20 · 2021: 7
- FWCI
- 20.90
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Parallel, Distributed and Grid Computing (PDGC) (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- stock price prediction, LSTM, support vector regression, recurrent neural networks, time series forecasting, stock market indices, S&P 500, NASDAQ, New York Stock Exchange, NSE, bovine spongiform encephalopathy, Dow Jones Industrial Average, model comparison
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