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The stock market is inherently volatile and complex, making accurate prediction a challenging task. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have shown significant promise in modeling sequential data such as stock prices. This paper explores the use of LSTM networks for stock market prediction, focusing on their ability to capture temporal dependencies and non-linear patterns in financial time series. We present an overview of the LSTM architecture and discuss its advantages over traditional methods. Furthermore, a comprehensive literature review is conducted to analyze various implementations and performances of LSTM-based models in stock price forecasting. The goal is to establish a foundation for future research and implementation strategies in financial forecasting using deep learning.
Keywords:
Stock Market Prediction, Deep Learning, LSTM, Time Series Forecasting, Financial Data, Machine Learning.
Cite Article:
"Analysing Market factors for Stock Market Prediction using deep learning Techniques", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.a484-a488, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506055.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