It is called “Seasonal Naïve”. While the simple Naïve copies the last observed actual into the future as a forecast, the seasonal one copies the whole seasonal shape of the data and uses it as a forecast. The logic is straightforward: if you see an increase in sales every January, why not use the actual sales of January 2025 as the forecast for January 2026? Simple, easy to do, and hard to beat in some cases, especially when your demand does not have high variability.

What this method doesn’t do is filter out the noise in the data. This means that if you had a promotion-driven spike this February, Seasonal Naïve will happily copy it into next February’s forecast. So, if you have some distinct components in your time series and/or effects of explanatory variables on sales, Seasonal Naïve might not be a good choice. But it remains an essential benchmark for any seasonal data.

I actually have an anecdote related to the Seasonal Naïve. Yves Sagaert and I were working on a paper, and we decided to apply our new method to data with multiple seasonality. It worked great, better than the double seasonal exponential smoothing and ARIMA. I was really hyped and was ready to celebrate, when Yves suggested trying the Seasonal Naïve as well. It’s good that he did, because it turned out that Seasonal Naïve beat them all, including our new method, without even blinking. This was a great demonstration of a principle I had been preaching to others: if you have seasonal data, always use Seasonal Naïve as a benchmark.

In the Demand Forecasting Principles course in November, we cover simple methods like this one properly, including when to stop trusting them. Details and booking can be found here.

Message Another important Naïve method first appeared on OpenForecast.

Naïve 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 Naïve, 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 Naïve 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 Naïve is not a good benchmark, read this post.

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