Syllabus

Module (CO) 1:

  • Introduction to Forecasting
    • Importance of Forecasting
    • Types of Forecasting
    • Data Types in Forecasting
  • Statistical Concepts in Time Series Analysis
    • Basic R Functions for Time Series Analysis
    • Exploring Data Patterns
    • Time Series and their components

Module (CO) 2:

  • Methods for Forecasting
    • Moving Averages and Exponential Smoothing
    • Smoothing of Annual Time Series
  • Least-Squares Trend Fitting and Forecasting
    • Linear, quadratic, and exponential models

Module (CO) 3:

  • Autocorrelation and Autoregressive Models
    • ARIMA/SARIMA
  • Time-series Models for Measuring Volatility
    • ARCH/GARCH Models
  • Time-Series Forecasting of Monthly or Quarterly Data
    • Accuracy Statistics and Forecast Model Selection
    • Hybrid Models

Module (CO) 4:

  • Hierarchical Forecasting

  • Adjustments to Statistical Forecasts

    • Event variables, outlier variables, and other model inputs