Comprehensive Project Requirements Document

Table of Contents

1. Introduction
   • 1.1 Project Overview
   • 1.2 Objectives
   • 1.3 Scope
2. Business Logic and Requirements
   • 2.1 Understanding Stock Market Dynamics
   • 2.2 Business Objectives
   • 2.3 Stakeholders
3. Data Requirements
   • 3.1 Data Sources
   • 3.2 Data Collection Methods
   • 3.3 Data Preprocessing
   • 3.4 Feature Engineering
4. Modeling Approaches
   • 4.1 Traditional Statistical Methods
   • 4.2 Machine Learning Algorithms
   • 4.3 Deep Learning Models
   • 4.4 Transformer-based Models
   • 4.5 Ensemble Methods
5. Model Optimization and Evaluation
   • 5.1 Optimization Techniques
   • 5.2 Evaluation Metrics
   • 5.3 Cross-Validation Strategies
   • 5.4 Hyperparameter Tuning
6. Deployment Strategy
   • 6.1 Infrastructure Requirements
   • 6.2 Model Serving
   • 6.3 Monitoring and Maintenance
7. Project Management
   • 7.1 Timeline
   • 7.2 Milestones
   • 7.3 Team Roles and Responsibilities
8. Risk Management
   • 8.1 Potential Risks
   • 8.2 Mitigation Strategies
9. Compliance and Ethics
   • 9.1 Data Privacy
   • 9.2 Ethical Considerations
10. Conclusion

1. Introduction

1.1 Project Overview

The Stock Price Prediction Project aims to develop a robust, scalable, and accurate system for forecasting stock prices using advanced AI techniques. Leveraging state-of-the-art algorithms, including machine learning and deep learning models, particularly transformer architectures, the project seeks to provide actionable insights for investors, financial analysts, and stakeholders.

1.2 Objectives

• Accuracy: Achieve high predictive accuracy for short-term and long-term stock price movements.