Day 1: Introduction to Financial Forecasting and AI

• Overview of financial forecasting techniques
• Limitations of traditional methods
• Introduction to artificial intelligence and machine learning
• Key concepts and terminology

Day 2: Data Preparation and Feature Engineering

• Financial data sources and collection
• Cleaning and preprocessing data
• Feature selection and engineering techniques
• Time-series data fundamentals

Day 3: Machine Learning Models for Forecasting

• Regression models (linear, multiple, Lasso, Ridge)
• Time-series models (ARIMA, SARIMA)
• Neural networks and deep learning basics
• Model evaluation and validation

Day 4: Practical Applications and Case Studies

• Real-world case studies using AI in financial forecasting
• Building forecasting models with Python or Excel-based tools
• Scenario analysis and stress testing
• Ethics and risks in AI forecasting

Day 5: Project Day and Final Assessment

• Group or individual projects: developing an AI-based financial forecast
• Presentations and feedback
• Review of key concepts
• Final Q&A and course wrap-up