It’s inevitable, when making a forecast or a model (meant in the broadest possible terms), that you will be wrong—“all models are wrong,”1 after all. The obvious challenge is to convince your audience that you are doing something they can use, even if your model is wrong.
An essential tool in these circumstances is the ability to explain your model to its audience or users. Without some form of explanation for how the input variables are influencing the model’s output, you can’t make any kind of hypothesis about what is happening. Without a hypothesis ...
