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Introduction

Stock price prediction has been a topic of interest for both academia and industry due to its potential impact on decision-making processes in financial markets. With the increasing availability of historical stock price data and advancements in machine learning techniques, researchers have explored various approaches to predict stock prices accurately. Long Short-Term Memory (LSTM), a type of recurrent neural network (RNN), has shown promising results in time series forecasting tasks, making it a suitable candidate for building a stock price prediction system.

This thesis aims to develop a stock price prediction system using LSTM to forecast future stock prices accurately. The system will leverage historical stock price data, technical indicators, and other relevant features to train the LSTM model. By incorporating LSTM’s ability to capture long-term dependencies in sequential data, we expect the system to outperform traditional time series forecasting models in predicting stock prices.

Table of Contents

1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of stock price prediction methods
2.2 Traditional time series forecasting models
2.3 Machine learning techniques for stock price prediction
2.4 Long Short-Term Memory (LSTM) in time series forecasting
2.5 Applications of LSTM in financial markets
2.6 Challenges in stock price prediction
2.7 Evaluation metrics for forecasting models
2.8 Feature engineering for stock price prediction
2.9 Data preprocessing techniques
2.10 Summary of literature review

Chapter 3: System Design and Methodology
3.1 Data collection and preprocessing
3.2 Feature selection and engineering
3.3 Long Short-Term Memory (LSTM) model architecture
3.4 Training and evaluation process
3.5 Hyperparameter tuning
3.6 Ensemble methods for model improvement
3.7 Model interpretation techniques
3.8 Testing and validation
3.9 Performance evaluation metrics
3.10 Summary of system design and methodology

Chapter 4: System Implementation
4.1 Software tools and libraries
4.2 Data visualization techniques
4.3 Model training and testing environment
4.4 Deployment of the prediction system
4.5 Performance monitoring and feedback loop
4.6 System scalability and efficiency
4.7 Security considerations
4.8 User interface design
4.9 Integration with existing trading platforms
4.10 Summary of system implementation

Chapter 5: Conclusion and Summary
5.1 Recap of research objectives
5.2 Summary of findings
5.3 Implications for future research
5.4 Recommendations for practitioners
5.5 Conclusion and final remarks

This thesis overview on Building a stock price prediction system using LSTM aims to provide a comprehensive overview of the research topic, including the background, problem statement, objectives, methodology, system design, implementation, and conclusions. Through this study, we seek to contribute to the existing body of knowledge on stock price prediction and demonstrate the potential of LSTM in forecasting financial time series data.

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