Training on Time Series Forecasting for Economic and Public Health Planning

Most crises don't arrive unannounced.

The warning signs were usually there.

Hospital admissions were trending upward.

Inflation pressures were building.

Disease outbreaks were spreading quietly.

Food prices were becoming unstable.

Revenue collections were declining.

The data was talking.

Nobody was listening.

Or worse, nobody knew how to interpret what it was saying.

Forecasting isn't about predicting the future with perfect accuracy.

It's about reducing uncertainty before uncertainty becomes expensive.

The best planners don't own crystal balls.

They build better models.

In this course, you'll learn how to:

• Forecast economic, health, and social indicators with confidence
• Identify trends, seasonality, cycles, and emerging risks before they become crises
• Apply statistical and AI-driven forecasting techniques to real-world planning challenges
• Build forecasting systems that support evidence-based policy and resource allocation
• Communicate uncertainty and forecast results effectively to decision-makers

And yes, we'll discuss why many organizations still react to problems that their own data predicted months ago.

Overview

Governments, public health agencies, central banks, development organizations, healthcare systems, and private-sector institutions increasingly rely on forecasting to support strategic planning, resource allocation, risk management, and policy development. Accurate forecasts enable organizations to anticipate future conditions, prepare for emerging challenges, and make proactive rather than reactive decisions.

Economic and public health systems are influenced by complex and dynamic factors including demographic changes, disease outbreaks, labor market conditions, inflation, migration, environmental risks, healthcare utilization, consumer behavior, and policy interventions. Understanding how these variables evolve over time requires specialized analytical techniques capable of identifying trends, cycles, seasonality, structural changes, and uncertainty.

Time series forecasting provides a powerful framework for analyzing historical data and generating evidence-based predictions. Modern forecasting increasingly combines traditional statistical methods with machine learning, artificial intelligence, real-time data streams, and advanced computational approaches to improve accuracy and responsiveness.

This course equips participants with practical and strategic expertise in time series analysis, forecasting methodologies, predictive analytics, model evaluation, and decision support. Participants will learn how to develop, validate, interpret, and communicate forecasts that inform economic planning, public health preparedness, social policy development, and organizational strategy.

The program integrates statistics, econometrics, epidemiology, machine learning, business intelligence, risk analysis, and policy planning into a comprehensive framework for predictive decision-making.

Through practical forecasting exercises, data analysis workshops, simulation activities, software demonstrations, and real-world case studies, participants will develop the capability to apply forecasting techniques to a wide range of economic and public health challenges.

Duration

10 Days

Who Should Attend

• Economists and economic planners
• Public health professionals
• Epidemiologists
• Government statisticians
• Policy analysts
• Monitoring and evaluation specialists
• Data scientists and analysts
• Central bank professionals
• Healthcare planning managers
• Development practitioners
• Researchers and academics
• Strategic planning professionals

Individual Impact

• Strengthen quantitative and forecasting skills
• Improve analytical and predictive modeling capabilities
• Enhance policy evaluation and planning competencies
• Develop expertise in modern forecasting tools
• Improve decision-support and communication skills
• Gain practical experience with AI-enabled forecasting techniques

Organizational Impact

• Improve strategic planning and preparedness
• Strengthen evidence-based decision-making
• Enhance public health and economic forecasting capacity
• Improve resource allocation and budgeting
• Increase resilience to emerging risks and disruptions
• Support proactive policy development
• Strengthen monitoring, evaluation, and planning systems

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

• Understand the principles of time series analysis
• Identify trends, seasonality, cycles, and structural changes
• Apply classical and modern forecasting methods
• Build forecasting models for economic and health indicators
• Evaluate forecast accuracy and reliability
• Use machine learning and AI for predictive analytics
• Communicate forecast uncertainty effectively
• Support evidence-based planning and policymaking
• Develop forecasting systems and workflows
• Integrate forecasts into decision-making processes

Module 1: Foundations of Time Series Analysis

• Understanding time series data
• Components of time series
• Trend, seasonality, cycles, and irregular variation
• Forecasting principles and applications
• Data preparation and quality assessment
• Exercise: Exploring time series datasets
• Case Study: Forecasting in policy environments

Module 2: Descriptive Analysis and Visualization

• Time series visualization techniques
• Identifying patterns and anomalies
• Seasonal decomposition methods
• Structural break detection
• Exploratory forecasting approaches
• Practical: Visual analytics workshop
• Case Study: Public health surveillance data

Module 3: Classical Forecasting Methods

• Moving averages
• Exponential smoothing techniques
• Holt and Holt-Winters models
• Forecast generation and interpretation
• Strengths and limitations of classical methods
• Exercise: Forecasting with smoothing techniques
• Case Study: Budget and expenditure forecasting

Module 4: ARIMA and Statistical Forecasting Models

• Autoregressive processes
• Moving average processes
• ARIMA model development
• Stationarity testing and transformation
• Model diagnostics and validation
• Practical: Building ARIMA forecasts
• Case Study: Economic indicator forecasting

Module 5: Seasonal and Multivariate Forecasting

• Seasonal ARIMA (SARIMA)
• Vector autoregression (VAR)
• Multivariate forecasting approaches
• Incorporating external predictors
• Dynamic forecasting techniques
• Exercise: Multivariate forecasting workshop
• Case Study: Health demand forecasting

Module 6: Forecast Evaluation and Uncertainty Analysis

• Forecast accuracy measures
• Error metrics and benchmarking
• Prediction intervals and confidence bands
• Scenario analysis techniques
• Communicating uncertainty to decision-makers
• Practical: Forecast evaluation exercises
• Case Study: Planning under uncertainty

Module 7: Machine Learning for Forecasting

• Machine learning forecasting concepts
• Tree-based forecasting models
• Ensemble forecasting approaches
• Deep learning fundamentals
• Forecast automation techniques
• Exercise: AI-based forecasting applications
• Case Study: Predictive analytics in government

Module 8: Public Health Forecasting Applications

• Disease surveillance and outbreak forecasting
• Hospital demand forecasting
• Resource and workforce planning
• Vaccination and intervention forecasting
• Epidemiological forecasting considerations
• Practical: Public health forecasting project
• Case Study: Pandemic response planning

Module 9: Economic Forecasting Applications

• Inflation forecasting
• Employment and labor market projections
• Revenue and tax forecasting
• Macroeconomic indicator forecasting
• Policy scenario modeling
• Exercise: Economic forecasting workshop
• Case Study: National planning and budgeting

Module 10: Forecasting Systems for Decision Support

• Forecasting governance frameworks
• Operational forecasting systems
• Dashboard integration and automation
• AI-assisted forecasting environments
• Future trends in predictive analytics
• Capstone Exercise: Economic or Public Health Forecasting Project
• Case Study: Forecast-driven policymaking