I just published a longer case study, Modeling Short Time Series with Prior Knowledge: What ‘Including Prior Information’ really looks like.
It is generally difficult to model time series when there is insuffient data to model a (suspected) long seasonality. We show how this difficulty can be overcome by learning a seasonality on a different, long related time series and transferring the posterior as a prior distribution to the model of the short time series. The result is a forecast that is believable and can be used for decisions in a business context. In contrast to traditional methods that are not able to incorporate the long seasonality, we observe a drastic increase in common evaluation metrics. Default models in the
prophet R packages fail to produce good forecasts on this example.
Find the case study here, and the data and code on Github.