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 […]
When you start working with your data and suddenly realise that there are zeroes there, i.e. it is intermittent demand, what should you do first? Some people use SBC classification, but is that what you need? Let’s discuss! Intermittent demand comes in different flavours: sometimes zeroes occur frequently with low demand volumes, while other times […]
Sometimes, when you need to forecast demand, you may notice that the recorded data contains zeroes. There are several possible reasons for this, but today we’ll briefly discuss one of them. Welcome to the world of “intermittent demand”! Intermittent demand is the demand that happens at irregular frequency (Svetunkov & Boylan, 2023). This means you […]
Authors: Ivan Svetunkov, John E. Boylan Journal: International Journal of Production Economics Abstract: Inventory decisions relating to items that are demanded intermittently are particularly challenging. Decisions relating to termination of sales of product often rely on point estimates of the mean demand, whereas replenishment decisions depend on quantiles from interval estimates. It is in this […]
I met John in 2014 when he joined the Department of Management Science at Lancaster University. Back then, I was in my second year of PhD, and as a teaching assistant, I helped deliver workshops for some modules. We met at the departmental Christmas party, and John asked me whether I was the very same […]
UPDATE: I have also written a short post on “The role of M competitions in forecasting“, which gives historical perspective and a brief overview of the main findings of the previous competitions. Some of you might have noticed that the guidelines for the M5 competition have finally been released. Those of you who have previously […]
More than 2 years ago I published on this website a working paper entitled “Multiplicative State-Space Models for Intermittent Time Series“, written by John Boylan and I. This was an early version of the paper, which we submitted to International Journal of Forecasting on 31st January 2017. More than two years later (on 11th July […]
In one of the previous posts, we have discussed how to measure the accuracy of forecasting methods on the continuous data. All these MAE, RMSE, MASE, RMSSE, rMAE, rRMSE and other measures can give you an information about the mean or median performance of forecasting methods. We have also discussed how to measure the performance […]