The Joint strategy
The previous two strategies consider a model to have a single output. This is the case with most ML models; we formulate the model to predict a single scalar value after taking in an array of inputs: multiple input, single output (MISO). But there are some models, such as the DL models, which can be configured to give us multiple output. Therefore, the joint strategy, also called multiple input, multiple output (MIMO), aims to learn a single model that produces the entire forecasting horizon as output:
Figure 18.4: Joint strategy for multi-step forecasting
Let’s see how these regimes work.
Training regime
The joint strategy involves training a single multi-output model to forecast all the timesteps in the horizon at once. We can see in Figure 18.4 that we use the window function, W(Yt), to draw a window from Yt and train the model to predict yt+1,…, yt+H. During training, a loss function that measures the divergence between all...
