There is another forecasting method that is extremely popular, hard to beat, and has no parameters to estimate. It also has “Naïve” in its name. Do you know what I’m talking about? It is called “Seasonal Naïve”. While the simple Naïve copies the last observed actual into the future as a forecast, the seasonal one … Read more
There is one forecasting method that appears more often than any other in competitions and evaluations. An experienced forecaster will always use it as a benchmark. This method is called “Naïve”, and here is why it is popular and important. Naïve is a very simple forecasting method: the forecast equals to the last observed value. … Read more
Our demand forecasting course is back! This time under the OpenForecast umbrella. Since 2024, Kandrika Pritularga and I have run a demand forecasting principles course at the Centre for Marketing Analytics and Forecasting. We ran it three times, and it has received good reviews from the participants. They liked that the material was motivated by … Read more
One of the most common questions we get from companies is “How much safety stock should we hold?” Companies usually want simple solutions, but the honest answer is that there is no universal number and no universal formula – depending on what you sell, how often you sell, how your operations are setup etc, you … Read more
Many of you have seen this image on the Internet — I’ve seen it myself a few times on LinkedIn lately. People say it depicts the “Dunning-Kruger” effect… But did you know this is actually an internet meme with little to do with the original paper? Here is one of the recent examples, a screenshot … Read more
Sometimes I see people referring to a “deterministic” forecast, and I have some personal issues with this. Because if you apply a model to data then there is nothing deterministic about your forecasts! In many contexts, “deterministic” has a precise meaning: no randomness, no uncertainty. A deterministic solution to an optimisation problem (e.g. linear programming) … Read more
Apparently, we need to talk about scaling of error measures because this is not as obvious as it seems. In forecasting literature, since early days of the area, there has been a general consensus that the forecast errors from the individual time series should not be analysed and aggregated as is. This is because you … Read more
What do you use for model selection? Do you select the best model based on its cross-validated performance, or do you use in-sample measures like AIC? If so, there is a way to improve your selection process further. JORS recently published the paper of Nikos Kourentzes and I based on a simple but powerful idea: … Read more
I’ve been acting as a reviewer lately, providing comments on papers about intermittent demand, and I’ve felt a bit frustrated by what some authors write. Let me explain. Several papers I reviewed claim that demand can be either intermittent or lumpy. They then mention the Syntetos-Boylan-Croston (SBC) classification and use the thresholds from Syntetos et … Read more
Everything is random! Your data, your model, its parameter estimates, the forecasts it produces, and even the minimum of the loss function you used. There is no such thing as a “deterministic” forecast – everything is stochastic! Whenever you work with data, you are working with a sample from a population. In some cases, this … Read more