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 real-life problems, that we explained it in detail, answered all the questions, and had helpful workshops. So, having discussed this with Kandrika and Nikos, we decided to continue the course, but now under the umbrella of OpenForecast, and this time Nikos Kourentzes and I will be teaching it together.

Demand Forecasting Principles is a four-week online course running in October 2026. We plan to have eight live Zoom sessions (two per week), with hands-on workshops in both R and Python between them. All sessions are recorded and shared with the participants for their reference.

The course will cover all the essentials of forecasting, starting from the forecasting principles, moving to the simple methods, ETS, intermittent demand, forecast evaluation, judgement, and finishing with forecast combinations.

Who is this for?

• demand planners,
• data scientists,
• business analysts
• anyone who works with forecasts and wants to understand what happens behind the numbers.

What do you need to know?

You don’t need prior knowledge of forecasting or statistics – we explain everything as we go, in as much detail as you need. Yet, experienced practitioners will still find plenty that is new: some of our previous participants were seasoned forecasters and said exactly that.

The details, programme, prices and booking are available here. Students get a discount.

Exact October dates will be announced next week, but you can already book your place.

If you are not sure whether the course fits you or your team, get in touch.

Message Demand Forecasting Principles course, October 2026 first appeared on OpenForecast.

JORS recently published the paper of Nikos Kourentzes and I based on a simple but powerful idea: instead of using summary statistics (like the mean RMSE of cross-validated errors), you should consider the entire distribution and choose a specific quantile. This aligns with my previous post on error measures, but here is the core intuition:

The distribution of error measures is almost always asymmetric. If you only look at the average, you end up with a “mean temperature in the hospital” statistic, which doesn’t reflect how models actually behave. Some models perform great on most series but fail miserably on a few.

What can we do in this case? We can look at quantiles of distribution.

For example, if we use 84th quantile, we compare the models based on their “bad” performance, situations where they fail and produce less accurate forecasts. If you choose the best performing model there, you will end up with something that does not fail as much. So your preferences for the model become risk-averse in this situation.

If you focus on the lower quantile (e.g. 16th), you are looking at models that do well on the well-behaved series and ignore how they do on the difficult ones. So, your model selection preferences can be described as risk-tolerant, because you are accept that the best performing model might fail on a difficult time series.

Furthermore, the median (50th quantile, the middle of sample), corresponds to the risk-neutral situation, because it ignores the tails of the distribution.

What about the mean? This is a risk-agnostic strategy, because it says nothing about the performance on the difficult or easy time series – it takes everything and nothing in it at the same time, hiding the true risk profile.

So what?

In the paper, we show that using a risk-averse strategy tends to improve overall forecasting accuracy in day-to-day situations. Conversely, a risk-tolerant strategy can be beneficial when disruptions are anticipated, as standard models are likely to fail anyway.

So, next time you select a model, think about the measure you are using. If it’s just the mean RMSE, keep in mind that you might be ignoring the inherent risks of that selection.

P.S. While the discussion above applies to the distribution of error measures, our paper specifically focused on point AIC (in-sample performance). But it is a distance measure as well, so the logic explained above holds.

P.P.S. Nikos wrote a post about this paper here.

P.P.P.S. And here is the link to the paper.

Message Risky business: how to select your model based on risk preferences first appeared on OpenForecast.