Paper Title
Stock Price Prediction
Article Identifiers
Registration ID: IJNRD_300035
Published ID: IJNRD2409144
: http://doi.one/10.1729/Journal.41733
Authors
Keywords
LSTM, Bi-LSTM, DL, ML, SVM, RMSE, MSE, R2
Abstract
Predicting Stock market is actually one of the major type of tasks. There are so many traditional techniques for predicting stock prices but they have achieved poor accuracy because they are unable to identify intricate patterns and nonlinear correlations in the data. By utilizing cutting-edge deep learning methods like LSTM and BiLSTM with Attention Mechanism, our model effectively captures nonlinear relationships and temporal dependencies in the data, allowing it to overcome these restrictions. This study uses deep learning (DL) and machine learning (ML) approaches to provide a thorough examination of stock price prediction. Yahoo Finance is used to gather daily historical stock data spanning the last five years. Training sets make up 80% of the dataset, whereas testing sets make up 20%. Metrics like Root Mean Squared Error (RMSE), Mean Squared Error (MSE) and R-squared (R2) are used to train and assess machine learning models like Support Vector Machine (SVM) Regressor and Random Forest Regressor as well as deep learning models like Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Stacked BiLSTM with Attention Mechanism. With the highest accuracy percentage, the Stacked BiLSTM model with Attention Mechanism is the best-performing model.
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How To Cite (APA)
Abhishek Kumar & Sheetal Pereira (September-2024). Stock Price Prediction. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(9), b368-b377. http://doi.one/10.1729/Journal.41733
Issue
Volume 9 Issue 9, September-2024
Pages : b368-b377
Other Publication Details
Paper Reg. ID: IJNRD_300035
Published Paper Id: IJNRD2409144
Downloads: 000122253
Research Area: Science and Technology
Author Type: Indian Author
Country: Kalyan, Maharashtra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2409144.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2409144
Crossref DOI: http://doi.one/10.1729/Journal.41733
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