Here is a curious idea: if we can somehow estimate the importance of trend/seasonal components for your data, you can use this in model building and forecasting. But how can we do this first step? Hans Levenbach has an answer with his simple EDA technique. Let me explain. The core idea is simple and neat. … 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
When I read posts written by some ML experts, I sometimes notice that they either overlook or do not clearly explain a few crucial steps in demand forecasting. In this post, I want to highlight the three most important ones based on my personal experience. First and foremost, stationarity (see the proper definition of the … Read more
Here is another fascinating aspect of the seasonal profile in your data: it can evolve over time due to changing consumer preferences. How so? Let me explain. I’ve worked with a couple of companies where there were some examples of data with drastically changing seasonal patterns over just a few years. For example, Before Covid … Read more
A couple of days ago, I noticed a link to the following paper in a post by Jack Rodenberg: https://arxiv.org/abs/2506.05941v1. The topic seemed interesting and relevant to my work, so I read it, only to find that the paper contains several serious flaws that compromise its findings. Let me explain. Introduction But first, why am … 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
For the last year, Anna Sroginis and I have been working on a paper, trying to modernise demand classification schemes and make them useful in the brave new era of machine learning. We have finally wrapped it up and submitted it to a peer-reviewed journal. But the temptation to share was too strong, so we … Read more