By the end of this program, participants will be able to:

The main objective of this program is to provide participants with practical knowledge and hands-on skills to apply AI techniques such as machine learning and predictive analytics in preparing accurate and dynamic financial forecasts. By the end of the program, participants will be able to build, interpret, and evaluate AI-driven forecasting models suitable for real-world financial environments.

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