Tutorial learning objectives - Week 6

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

• Explain the fundamentals of exponential smoothing, including key concepts, forecasting and smoothing equations, and the role of parameters.

• Implement Simple Exponential Smoothing (SES) using a for loop, writing out both the forecast and smoothing equations in R.

• Able to differentiate between custom optimization-based forecasting and automated ETS modeling by modifying a forecasting function to minimize the sum of squared errors using the optim() function, and comparing the estimated parameters to those obtained from the ETS() function in R.

• Understand the trend component of exponential smoothing