Machine Learning for Predicting Stock Market Trends using Time Series Analysis in Mathematics

Thesis Abstract

Abstract
This thesis explores the application of machine learning techniques for predicting stock market trends using time series analysis within the realm of mathematics. The stock market is a complex system driven by various factors, making it challenging to predict trends accurately. Machine learning algorithms offer a promising approach to analyze historical stock market data and extract patterns that can help predict future trends. Time series analysis provides a powerful framework for modeling sequential data, making it particularly well-suited for analyzing stock market data, which is inherently sequential in nature. Chapter 1 introduces the research topic, providing background information on the stock market, machine learning, and time series analysis. The problem statement highlights the challenges of predicting stock market trends, while the objectives of the study outline the specific goals to be achieved. The limitations and scope of the study delineate the boundaries within which the research will be conducted. The significance of the study emphasizes the potential impact of using machine learning for stock market prediction, and the structure of the thesis provides an overview of the chapters to follow. Finally, the definition of terms clarifies key concepts used throughout the thesis. Chapter 2 presents a comprehensive literature review covering ten key aspects related to machine learning, time series analysis, and stock market prediction. The review synthesizes existing research findings and identifies gaps in the current literature, laying the foundation for the subsequent chapters. Chapter 3 details the research methodology employed in this study, including data collection, preprocessing, feature selection, model training, and evaluation. The chapter also discusses the selection of machine learning algorithms and time series analysis techniques, as well as the criteria for evaluating the predictive performance of the models. Eight sub-sections provide a step-by-step guide to the research methodology used in this study. Chapter 4 delves into an elaborate discussion of the findings obtained from applying machine learning and time series analysis to predict stock market trends. The chapter analyzes the performance of different machine learning models, evaluates the effectiveness of time series analysis techniques, and discusses the implications of the results in the context of stock market prediction. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and suggesting avenues for future work. The conclusion highlights the significance of using machine learning for predicting stock market trends and reflects on the limitations of the study. The summary encapsulates the main contributions of the research and underscores the potential for further advancements in this field. In conclusion, this thesis contributes to the growing body of research on using machine learning and time series analysis in predicting stock market trends. By leveraging advanced computational techniques, this study demonstrates the potential for enhancing stock market prediction accuracy and providing valuable insights for investors and financial analysts. The findings of this research offer a foundation for further exploration and development in the field of financial forecasting and algorithmic trading strategies.