| Abstract: | The evidence from the literature on forecast combination shows that combinations generally perform well. We discuss here how the accuracy and diversity of the methods being combined and the robustness of the combination rule can influence performance, and illustrate this by showing that a simple, robust combination of a subset of the nine methods used in the M4 competition’s best combination performs almost as well as that forecast, and is easier to implement. We screened out methods with low accuracy or highly correlated errors and combined the remaining methods using a trimmed mean. We also investigated the accuracy risk (the risk of a bad forecast), proposing two new accuracy measures for this purpose. Our trimmed mean and the trimmed mean of all nine methods both had lower accuracy risk than either the best combination in the M4 competition or the simple mean of the nine methods. |
