Stock Prediction System Using ML


Authors : Akash Kumar; Garima Panwar; Anant Samrat

Volume/Issue : Volume 9 - 2024, Issue 12 - December

Google Scholar : https://tinyurl.com/mrrahe9f

Scribd : https://tinyurl.com/3fmdtdtm

DOI : https://doi.org/10.5281/zenodo.14471364

Abstract : The stock market is a complex and dynamic system characterized by significant volatility and uncertainty[1]. Accurate prediction of stock prices is crucial for investors and financial analysts to make informed decisions and maximize returns. Traditional forecasting methods often fall short due to their reliance on historical data alone and their inability to adapt to rapid market changes. In recent years, machine learning (ML) has emerged as a powerful tool for enhancing stock prediction accuracy by leveraging advanced algorithms and large datasets. This paper presents a comprehensive study on the development and evaluation of a stock prediction system utilizing machine learning techniques. The system is designed to analyze historical stock price data and generate forecasts using two prominent ML models: Linear Regression and Long Short-Term Memory (LSTM) networks. Linear Regression is employed as a baseline model due to its simplicity and interpretability, while LSTM networks are utilized for their ability to capture complex temporal dependencies in time series data.

Keywords : Stock Prediction, Feature Selection, Jellyfish Optimization, Machine Learning, SVM.

References :

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The stock market is a complex and dynamic system characterized by significant volatility and uncertainty[1]. Accurate prediction of stock prices is crucial for investors and financial analysts to make informed decisions and maximize returns. Traditional forecasting methods often fall short due to their reliance on historical data alone and their inability to adapt to rapid market changes. In recent years, machine learning (ML) has emerged as a powerful tool for enhancing stock prediction accuracy by leveraging advanced algorithms and large datasets. This paper presents a comprehensive study on the development and evaluation of a stock prediction system utilizing machine learning techniques. The system is designed to analyze historical stock price data and generate forecasts using two prominent ML models: Linear Regression and Long Short-Term Memory (LSTM) networks. Linear Regression is employed as a baseline model due to its simplicity and interpretability, while LSTM networks are utilized for their ability to capture complex temporal dependencies in time series data.

Keywords : Stock Prediction, Feature Selection, Jellyfish Optimization, Machine Learning, SVM.

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