If someone tells you that method X solves all problems and is the best one ever, they are either lying intentionally or do not fully understand what they are talking about. There is no such thing as “the best approach for everything”. Let me explain. Consider two products sold by retailers: ice cream and bread. […]
If you follow certain influencers on LinkedIn, you might have come across the term “forecasting academia” (or “applied forecasting academia”). If you’re not familiar with the field, you might not know who this refers to, so I decided to write a short post about it. “Forecasting academia” refers to researchers working in the field of […]
Image above depicts a fashion model making a presentation about a forecasting method. I like the forecast for the final period in that image… Over the last few years, I’ve seen phrases like “LightGBM model” or “Neural Network model” on LinkedIn many times, and the statistician in me shivers every time. So, I figured it’s […]
I’m currently doing a literature review for one of my papers on intermittent demand forecasting with machine learning, and I’ve noticed a recurring fundamental mistake in several recently published papers, even in respectable peer-reviewed journals. The mistake? Using error measures based on the Mean Absolute Error (MAE). This is a crime against the humanity when […]
One thing that bothers me when I read posts on social media or papers in peer-reviewed journals is the claim that a proposed approach is “assumption-free.” In forecasting, this is never true. Such an approach is like a spherical unicorn in a vacuum (see image above). Here’s why. Every model is a simplification of reality, […]
Christmas and the New Year are upon us, and I wanted to publish a celebratory post before taking a break. Instead of writing something educational, I decided to simply recommend a paper for you to read over the holidays – something you might have overlooked in the past couple of years. Here it is or […]
I’ve seen several times ML experts applying principles of classification for intermittent demand forecasting. For example, they try predicting, WHEN the demand will happen. This is not a very sensible thing to do. The featured image in this post shows two forecasting approaches: one that tries to predict when demand happens (the yellow line), and […]
While Naive is considered a standard benchmark in forecasting, there is a case where it might not be a good one: intermittent demand. And here is why I think so. Naive is a forecasting method that uses the last available observation as a forecast for the next ones. It does not have any parameters to […]
Anna Sroginis and I have been working on a new approach for intermittent demand classification over the past year. We’ve taken a fresh look at the problem, starting by asking: why do zeroes happen? Let’s discuss why indeed. First, a quick note: it’s a mistake to define intermittent demand simply as “demand with zeroes”. That […]
Simple Moving Average (SMA) is one of the basic forecasting methods. It doesn’t rely on time series decomposition, doesn’t have a seasonal component, and doesn’t include explanatory variables. Yet, in a supply chain context, SMA is sometimes a tough benchmark to beat. Why? First things first, SMA is simply the arithmetic mean of several recent […]