Statistical Modelling & Forecasting Training Course

This course equips participants with advanced knowledge and practical skills in statistical modeling and forecasting techniques for data-driven decision-making. It covers regression models, time-series forecasting, multivariate methods, and modern forecasting tools. Participants will learn how to build, validate, and apply statistical models to predict future outcomes, analyze uncertainty, and support strategic and operational planning.

Target Groups

• Data analysts and statisticians
• Economists and financial analysts
• Business intelligence and forecasting professionals
• Risk managers and planners
• Researchers and academic professionals
• Policy analysts in government and public sector organizations
• Students pursuing advanced studies in statistics, data science, or economics

Course Objectives

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

• Understand the fundamentals of statistical modeling and forecasting.
• Apply regression techniques to explain and predict relationships.
• Develop reliable time-series forecasting models.
• Use multivariate methods for complex data analysis.
• Evaluate and validate forecasting models using appropriate metrics.
• Apply scenario-based forecasting for decision-making.
• Handle uncertainty and risk in forecasts.
• Integrate statistical models into business and policy planning.
• Use statistical software and programming tools for modeling.
• Communicate results effectively through reports and visualizations.

Course Modules

Module 1: Introduction to Statistical Modelling & Forecasting

• Role of statistics in forecasting and decision-making
• Types of models: deterministic vs. stochastic
• Forecasting process and stages
• Key concepts: error, variance, and uncertainty

Module 2: Data Preparation for Modelling

• Exploratory data analysis (EDA)
• Handling missing data and outliers
• Stationarity and transformations in time-series data
• Feature selection for forecasting models

Module 3: Regression Analysis for Forecasting

• Simple and multiple linear regression
• Logistic regression for categorical outcomes
• Model diagnostics and assumptions testing
• Forecasting applications of regression models

Module 4: Time-Series Forecasting Fundamentals

• Components of time-series: trend, seasonality, noise
• Smoothing techniques: moving averages, exponential smoothing
• Decomposition methods
• Identifying patterns in time-series data

Module 5: ARIMA and Advanced Time-Series Models

• AR, MA, ARMA, and ARIMA models
• SARIMA and seasonal adjustments
• Forecast accuracy evaluation
• Practical applications in finance, sales, and economics

Module 6: Multivariate Forecasting Models

• Vector Autoregression (VAR) models
• Cointegration and error correction models
• Multivariate regression and forecasting
• Applications in macroeconomic and financial modeling

Module 7: Forecasting with Machine Learning Approaches

• Decision trees and random forests for forecasting
• Gradient boosting models (XGBoost, LightGBM)
• Neural networks for time-series forecasting
• Hybrid statistical and ML models

Module 8: Model Validation and Accuracy Assessment

• Cross-validation techniques
• Forecast accuracy metrics (MAPE, RMSE, MAE)
• Overfitting vs. underfitting in forecasting models
• Model selection and comparison

Module 9: Risk, Uncertainty, and Scenario Forecasting

• Dealing with uncertainty in forecasting
• Monte Carlo simulations
• Scenario planning and sensitivity analysis
• Applications in financial and operational planning

Module 10: Case Studies and Practical Applications

• Real-world forecasting applications in business and policy
• Building forecasting models in R/Python
• Group project: end-to-end forecasting exercise
• Communicating results through reports and visualizations