Why Global Forecasting Models (GFMs)?

We talked about global models briefly in Chapter 5, Time Series Forecasting as Regression, where we mentioned related datasets. We can think of many scenarios where we would encounter related time series. We may need to forecast the sales for all the products of a retailer, the number of rides requested for a cab service across different areas of a city, or the energy consumption of all the households in a particular area (which is what the London Smart Meters dataset does). We call these related time series because all the different time series in the dataset can have a lot of factors in common with each other. For instance, the yearly seasonality that might occur in retail products might be present for a large section of products, or the way an external factor such as temperature affects energy consumption may be similar for a large number of households. Therefore, one way or the other, the different time series in a related time series dataset...