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 […]
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: […]
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 […]
Authors: Ivan Svetunkov, John E. Boylan Journal: IMA Journal of Management Mathematics Abstract: Exponential smoothing in state space form (ETS) is a popular forecasting technique, widely used in research and practice. While the additive error ETS models have been well studied, the multiplicative error ones have received much less attention in forecasting literature. Still, these […]
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 […]
Authors: Ivan Svetunkov, Nikos Kourentzes, Rebecca Killick Journal: Computational Statistics Abstract: Many modern statistical models are used for both insight and prediction when applied to data. When models are used for prediction one should optimise parameters through a prediction error loss function. Estimation methods based on multiple steps ahead forecast errors have been shown to […]
The paper Back in 2020, when we were all siting in the COVID lockdown, I had a call with Bahman Rostami-Tabar to discuss one of our projects. He told me that he had an hourly data of an Emergency Department from a hospital in Wales, and suggested writing a paper for a healthcare audience to […]
Authors: Bahman Rostami-Tabar, Jethro Browell, Ivan Svetunkov Journal: Health Systems Abstract: An accurate forecast of Emergency Department (ED) arrivals by an hour of the day is critical to meet patients’ demand. It enables planners to match ED staff to the number of arrivals, redeploy staff, and reconfigure units. In this study, we develop a model […]
Authors: Ivan Svetunkov Abstract: There are many forecasting related packages in R with varied popularity, the most famous of all being forecast, which implements several important forecasting approaches, such as ARIMA, ETS, TBATS and others. However, the main issue with the existing functionality is the lack of flexibility for research purposes, when it comes to […]
About the paper. Disclaimer The idea of using complex variables in modelling and forecasting was originally proposed by my father, Sergey Svetunkov. Based on that, we developed several models, which were then used in some of our research. We worked together in this direction and published several articles in Russian. My father even published a […]