Strategies to improve GFMs

GFMs have been in use in many forecasting competitions in Kaggle and outside of it. They have been battle-tested empirically, although very little work has gone into examining why they work so well from a theoretical point of view. Montero-Manso and Hyndman (2020) have a working paper titled Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality, which is an in-depth investigation, both theoretical and empirical, of GFMs and the many techniques that have been developed by the data science community collectively. In this section, we will try to include strategies to improve GFMs and, wherever possible, try to give theoretical justifications for why they would work.

Reference check

The Montero-Manso and Hyndman (2020) research paper is cited in References under reference number 1.

In the paper, Montero-Manso and Hyndman use a basic result in machine learning about generalization error to carry out the theoretical analysis...