STOCK PRICE PREDICTION USING LSTM
Abstract & Details
Research Area
computer science
Keywords
machine learning
deep learning
computer science
neural network
long short term memory
stock price analysis
time series analysis
prediction analysis
data modelling
RNN
CNN
Abstract
Our stock prediction project leverages machine learning (ML) and deep learning techniques to forecast stock prices with increased accuracy. By analyzing vast historical data, our model learns complex patterns and trends in stock market behavior. We employ recurrent neural networks (RNNs) and convolutional neural networks (CNNs) to capture temporal and spatial dependencies, respectively. Feature engineering and sentiment analysis of news and social media data further enhance predictions. Rigorous training and validation ensure robustness. Our approach aims to empower investors with valuable insights, helping them make informed decisions in the dynamic world of finance.
As we all know that the stock market is a very difficult market for investment and needs strong brainstorming before one shall start investing money in it. The field of stock price analysis requires immense research and deep knowledge of finance. Stock Market Analysis is one of the leading use of fundamentals of machine learning.
It is very difficult to find the future price of stock market because of the volatile nature of the market. Our work focuses on the LSTM model to predict the prices using strategic parameters of stock market like RMSE and MAPE. The lower the values of the parameter shows the higher accuracy of our model. In this work, to obtain better accuracy of the model, best of the features will be selected using different Feature selection methods.
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | HITESH SHUKLA | GALGOTIAS UNIVERSITY |
| 2 | SAHIL CHAUHAN | GALGOTIAS UNIVERSITY |
How to Cite
Use the following formats to cite this article in your research.
APA Style
SHUKLA, HITESH & CHAUHAN, SAHIL (2024). STOCK PRICE PREDICTION USING LSTM. International Journal of Advance Research and Innovative Ideas In Education, 10(2), 4304-4311.
MLA Style
SHUKLA, HITESH, and SAHIL CHAUHAN. "STOCK PRICE PREDICTION USING LSTM." International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 2, 2024, pp. 4304-4311.
IEEE Style
HITESH SHUKLA and SAHIL CHAUHAN, "STOCK PRICE PREDICTION USING LSTM," International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 2, pp. 4304-4311, 2024.
Vancouver Style
SHUKLA HITESH, CHAUHAN SAHIL. STOCK PRICE PREDICTION USING LSTM. International Journal of Advance Research and Innovative Ideas In Education. 2024;10(2):4304-4311.
Harvard Style
SHUKLA, HITESH & CHAUHAN, SAHIL (2024) 'STOCK PRICE PREDICTION USING LSTM', International Journal of Advance Research and Innovative Ideas In Education, 10(2), pp. 4304-4311.
Chicago Style
SHUKLA, HITESH and SAHIL CHAUHAN. "STOCK PRICE PREDICTION USING LSTM." International Journal of Advance Research and Innovative Ideas In Education 10, no. 2 (2024): 4304-4311.
Turabian Style
SHUKLA, HITESH and SAHIL CHAUHAN. "STOCK PRICE PREDICTION USING LSTM." International Journal of Advance Research and Innovative Ideas In Education 10, no. 2 (2024): 4304-4311.
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