Global Forecasting Models
In previous chapters, we saw how we can use modern machine learning models on time series forecasting problems, essentially replacing traditional models such as ARIMA or exponential smoothing. However, before now, we were looking at the different time series in any dataset (such as households in the London Smart Meters dataset) in isolation, just as the traditional models did.
However, we will now explore a different paradigm of modeling where we use a single machine learning model to forecast a bunch of time series together. As we will learn in the chapter, this paradigm brings many benefits with it, from the perspective of both computation and accuracy.
In this chapter, we will be covering these main topics:
- Why Global Forecasting Models (GFMs)?
- Creating GFMs
- Strategies to improve GFMs
- Bonus – interpretability
