Choosing between the random walk, the seasonal, or some other calculated version of the naive is a popular mini-controversy. The key point of the naïve is that it is simple to calculate and simpler to explain. If I found myself trying to explain how alpha works for a “naïve” exponential model, I suspect there’s a good chance that I would lose some key people in the audience, when I was supposed to be “keeping it simple.”

If we consider the naïve forecast as a “control” forecast, which is there only as a comparison for the stat forecast - we never actually forecast with it, then it’s probably okay if the naïve is a little dumb like a placebo in medical testing. In my book, the simplest “do nothing” type forecasts would be the seasonal and the random walk; both are very easy to explain.

But what happens if you use the “wrong” naïve?
Consider a product that is seasonal, but we use the random walk instead. Sure, the forecast accuracy of the naïve will be lower than it could have been, but does it still serve as a reasonable and consistent comparison for the stat model? I would say yes. Now what’s our conclusion if even though we used a not-so-good naïve, our stat model still failed to beat the naïve in 30%* of the cases? Forecast Value-added Analysis did its job; it pointed us to the areas where there is a potential for improvement. Now we can ask questions like, “Are the stat models too complicated and over-fitting the history? Are there non-repeating promotional events that should be cleaned out of the history?” and so on.

Since the goal of FVA is not to “optimize” the naïve forecast, but to drive improvement, my recommendation is to pick either the seasonal or the random walk and start using the tool to drive improvement.

* You'd be surprised how hard the naive is to beat when you start pulling real numbers!