Are there any PhD students in the crowd who want to learn more about forecasting? What about academic supervisors who have such students? Show me your hands! This post is for you! This September, we (Lancaster Centre for Marketing Analytics and Forecasting members) will deliver the Natcor course on Forecasting and Predictive Analytics at Lancaster […]
There’s a large and fascinating area in time series analysis called “changepoint detection”. I hadn’t worked in this area before, but thanks to Rebecca Killick and Thomas Grundy, I contributed to the paper “Online Detection of Forecast Model Inadequacies Using Forecast Errors“, which has just been published in the Journal of Time Series Analysis. DISCLAIMER: […]
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 […]
NHS has a helpful page with a set of questions you can ask your GP to ensure you receive the right treatment for your illness. Surprisingly, these questions can be applied in other fields as well. Here’s an example in applied forecasting, working with companies. I’m not going to go through all of them, I […]
If you have taken a course on forecasting or time series analysis, you’ve probably heard of ARIMA and the Box–Jenkins methodology. In my opinion, this methodology has a fundamental flaw and should not be used in practice. Here’s why. When Box and Jenkins wrote their book back in the 1960s, it was a very different […]
It was a pleasure to attend the 5th IMA and OR Society Conference at Aston University, Birmingham, and to present my research with Anna Sroginis on model-based demand classification. A great crowd of people from universities across the UK, along with several esteemed international colleagues. The event was very well organised – thanks to Aris […]
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 […]
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 […]
There are many different issues with capturing seasonality in time series. In this short post, I’d like to discuss one of the most annoying ones. I’m talking about the seasonal pattern that shifts over time. What I mean is that, for example, instead of having the standard number of observations in the cycle (e.g., 24 […]
In forecasting, the term seasonality doesn’t always mean what you think it does. It encompasses more than just patterns repeating from one season to the next. In fact, seasonality covers a wide range of periodic behaviors, and can have some issues associated with the naming conventions. Should we discuss? First things first: when we say […]