Tutorial learning objectives - Week 7

By the end of this tutorial, you should be able to:

1. Explain the Intuition Behind Exponential Smoothing

• Describe how exponential smoothing captures trend and seasonality.

• Interpret the roles of smoothing parameters and initial states in forecasting.

2. Compare Exponential Smoothing with Benchmark Models

• Identify similarities and differences between exponential smoothing and simpler models such as the mean and naïve forecasts.

• Recognize scenarios where exponential smoothing offers an advantage over these benchmark models.

3. Understand ETS Model Components

• Distinguish between additive and multiplicative forms of error, trend, and seasonality.

• Explain how these components work together in an ETS model.

• Analyze how smoothing parameters and initial states affect model behavior and forecast outputs.