Investigating the use of artificial intelligence in financial forecasting
Table Of Contents
<p>1. Introduction<br> 1.1 Background<br> 1.2 Problem Statement<br> 1.3 Objectives<br>2. Literature Review<br> 2.1 Traditional Financial Forecasting Methods<br> 2.2 Artificial Intelligence in Financial Forecasting<br> 2.3 Previous Studies and Findings<br>3. Methodology<br> 3.1 Data Collection<br> 3.2 Data Preprocessing<br> 3.3 AI Techniques for Financial Forecasting<br> 3.4 Model Training and Evaluation<br>4. Results and Analysis<br> 4.1 Performance Comparison with Traditional Methods<br> 4.2 Accuracy and Robustness of AI Models<br> 4.3 Interpretability of AI Models<br>5. Discussion<br> 5.1 Implications of AI in Financial Forecasting<br> 5.2 Limitations and Future Directions<br></p>
Thesis Abstract
<p> This project aims to explore the potential of artificial intelligence (AI) in financial forecasting. Traditional financial forecasting methods often rely on historical data and statistical models, which may not capture the complex dynamics of financial markets. By leveraging AI techniques such as machine learning and deep learning, this study seeks to develop more accurate and robust forecasting models. The project will involve collecting financial data, preprocessing and analyzing the data, training and evaluating AI models, and comparing their performance with traditional forecasting methods. The findings of this research can have significant implications for financial institutions and investors in making informed decisions. <br></p>Thesis Overview
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