Training on Time Series Forecasting for Economic & Policy Planning

Module 1: Fundamentals of Time Series Analysis

• Structure and components of time series (trend, seasonality, noise)

• Stationarity and data transformation techniques

• Visualizing and summarizing economic time series data

• Introduction to software tools: Excel, R, or Python

Module 2: Classical Forecasting Techniques

• Autoregressive (AR), Moving Average (MA), and ARMA models

• Autoregressive Integrated Moving Average (ARIMA) modeling

• Seasonal ARIMA (SARIMA) for cyclical economic data

• Model identification, estimation, and diagnostics

Module 3: Forecasting for Economic and Policy Indicators

• Time series models for GDP, inflation, and unemployment

• Forecasting fiscal revenues and expenditures

• Scenario-based policy simulations using forecasts

• Case study: Forecasting economic growth under uncertainty

Module 4: Advanced and Machine Learning Methods

• Vector Autoregression (VAR) and multivariate time series

• Time series regression and exogenous variable modeling

• Introduction to machine learning models (e.g., LSTM, random forest)

• Comparing model performance using validation techniques

Module 5: Communicating Forecasts for Policy Impact

• Presenting forecasts for policy and budget decisions

• Risk communication and uncertainty quantification

• Creating forecasting dashboards and visuals

• Real-life project: Forecasting and policy brief presentation