Advancements in Stock Price Prediction: Integrating Statistical, Machine Learning, and Deep Learning Models

Author(s)	Prof. DHANANJAY NARAYAN KALANGE
Country	India
Abstract	Forecasting stock prices has been a persistent challenge and focal area of research due to the volatile and complex nature of financial markets. Traditional statistical methods provided early insights into market behaviors, while recent advancements in machine learning (ML) and deep learning (DL) have enabled improved predictive accuracy through data-driven modeling. This paper reviews a broad spectrum of stock price forecasting methods across three major categories: statistical models, machine learning approaches, and deep learning architectures. We examine the theoretical foundations, performance, advantages, limitations, and real-world applications of each method. Additionally, a comparative analysis highlights the evolving landscape and the increasing integration of hybrid and ensemble techniques. This review includes references from seminal and recent works in the field.
Keywords	Stock Price Forecasting, Time Series Analysis, Machine Learning Models, Deep Learning Architectures, Financial Market Prediction
Field	Mathematics > Statistics
Published In	Volume 16, Issue 3, July-September 2025
Published On	2025-07-18
DOI	https://doi.org/10.71097/IJSAT.v16.i3.7103
Short DOI	https://doi.org/g9t2wr

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