For one of my customers I need to explain the concept of global models in simple words. Searching for simple introductions to the concept failed so far. All I can find are scientific studies, mostly about their advantage over local models.
What I need to explain is the basic functionality on how it works compared to local models. I know it is basically the same function, only that I am going to fit it to a group of time series instead of a single one. So if I have a set of 100 time series and want to predict the next 24 months, I need 100 iterations with local models (every column) and only 24 iterations (every forecast month) with global models.
How does this global forecast exactly happen, in terms of the data object and the loop function? I want to explain, why it is way faster than local models and why it is recommended to forecast on clusters of similar time series groups, to achieve higher accuracy.
Please share some insights or some good online references