Stock Price Prediction

Stock Price Prediction

Registration ID: IJNRD_300035

Published ID: IJNRD2409144

DOI: http://doi.one/10.1729/Journal.41733

Abhishek Kumar , Sheetal Pereira

LSTM, Bi-LSTM, DL, ML, SVM, RMSE, MSE, R2

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.

Published Paper   E-Certificate

"Stock Price Prediction", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 9, page no.b368-b377, September-2024, Available :https://ijnrd.org/papers/IJNRD2409144.pdf

Volume 9 Issue 9, September-2024

Pages : b368-b377

Paper Reg. ID: IJNRD_300035

Published Paper Id: IJNRD2409144

Downloads: 000121187

Research Area: Science and Technology

Country: Kalyan, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2409144.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2409144

DOI: http://doi.one/10.1729/Journal.41733

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

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