We agree that there is another half of this pie that you describe very well as that is what you do. What we do is the first half of the pie.

Where you say “Unknown and unknowable events have a large impact on your staffing”, our response is: Detecting when these events have occurred in the past and isolating their effect allows you to model in a more robust manner. Identifying these events leads naturally to heretofore unknown causal variables which might then aid modeling efforts. At a minimum, the distortion introduced by these is alleviated.

As for models failing, yes they do. Things change no doubt and how to model these “interventions” is key to being able to model these disruptions automatically so that the model will still predictably forecast the week AFTER the interruption is paramount.

The Monday hourly pattern is different than a Friday. The question is can you model them separately while taking into account the MACRO effects like holidays, etc. and level shifts and interventions. That’s where it gets a little a hairy and where our expertise comes into play.

Our analytic approach does not assume any prespecified weighting schemes but rather constructs a model based on the data: 1)optimal memory weighting (ie weekly/daily dummies) 2)lead/contemporaneous/lag structures around known holidays/events 3)4 Intervention variables which represent unspecified determinstic series (ie level shift, seasonal pulse, time trends and outliers)

The best way to evaluate alternative strategies like ours is to do so with a case study. We welcome the opportunity to do this.