Naive is a very simple forecasting method: the forecast equals to the last observed value. So, for example, if the room temperature at 12pm was 20 degrees Celsius, then Naive will forecast exactly the same temperature for 1pm, and it will usually be roughly right. That is what makes it dangerous to sophisticated models: it is often surprisingly hard to beat. If your model cannot beat Naive, it is not worth deploying, no matter how sophisticated and beautiful it is.

This is why there is a well-established rule in forecasting: any proper evaluation should include simple benchmarks, and Naive is the easiest one to implement, because it does not have any parameters to estimate and can work with the sample of one observation. This is why you will find it in every decent forecasting competition.

This is not a new finding, by the way. Back in 1979, Spyros Makridakis and Michèle Hibon published a paper, showing on a set of 111 real time series that simple methods were often at least as accurate as the sophisticated statistical ones. The audience did not take it well: the discussion that followed was openly hostile, with eminent statisticians suggesting that the results said more about the authors’ skills than about the methods. Spyros’ response was to test the claim at a much larger scale, which is how the M-competitions were born. I wrote a post about that some time ago. But the lesson survived the criticism: always compare your approach with the simple forecasting methods.

In our training course on Demand Forecasting Principles, we discuss this and other simple methods in more detail, showing where they work and where they fail. They are all building blocks for understanding applied forecasting.

The next course runs online in October, over Zoom. Details and booking can be found here.

P.S. There is one case where Naive is not a good benchmark, read this post.

Message Why Naive is popular and important first appeared on OpenForecast.