ABSTRACT
This chapter discusses the most common approaches, focusing on evaluating point forecasts, then moving towards prediction intervals and quantile forecasts. The main advantage of the error measures discussed in the subsection is that they are straightforward and have a clear interpretation: they reflect the “average” distances between the point forecasts and the observed values. There are several useful measures for the evaluation of intervals. While, in general, the selection of error measures should be dictated by the specific problem at hand, some guidelines might be helpful in the process. In R, there are several packages and functions that implement rolling origin. The rolling origin function from the greybox package also allows working with explanatory variables and returning prediction intervals if needed. There are different ways to present the test results, and there are several R functions that implement it, including nemenyi from the tsutils package.
