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Stabilizations of stock market prices vary rapidly,
and the market is unpredictable and hard to forecast the prices;
therefore, the need for robust time-varying models like the LSTM.
To enhance the quality of prediction, this study uses several
financial features to feed the model with more information and
richer features. It performs the required preprocessing, including
data cleaning, data normalization, and the generation of time
windows to make sure that the dataset is prepared well to be
trained in the LSTM architecture. A stacked LSTM network is
then trained to know both the short-term and long-term changes
in the price in the market. The model performance is measured
by RMSE, MAE, and R² in order to obtain a clear and precise
measurement of performance in terms of prediction accuracy.
The results indicate that LSTM is better than traditional models
like ARIMA and linear regression because it is more appropriate
for predicting stocks in a stock market.
Keywords:
Stock market prediction, long short-term memory (LSTM), deep learning, financial data analysis, Python programming, and machine learning techniques.
Cite Article:
"Stock Market Prediction Using Long Short-Term Memory (LSTM) Networks", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b492-b497, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604203.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator