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 […]
papers
Multi-step Estimators and Shrinkage Effect in Time Series Models
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 […]
Story of “Probabilistic forecasting of hourly emergency department arrivals”
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 […]
Probabilistic forecasting of hourly emergency department arrivals
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 […]
Complex Exponential Smoothing
Authors: Ivan Svetunkov, Nikolaos Kourentzes, Keith Ord. Journal: Naval Research Logistics Abstract: Exponential smoothing has been one of the most popular forecasting methods used to support various decisions in organisations, in activities such as inventory management, scheduling, revenue management and other areas. Although its relative simplicity and transparency have made it very attractive for research […]
Vector Exponential Smoothing with PIC restrictions
About the paper. Introduction When you follow academics on social media, you typically see many success stories. This person published a paper in Management Science; another one published in EJOR; your colleague from a different university created a great package; and there is also an academic who is ten years younger than you and has […]
A simple combination of univariate models
Fotios Petropoulos and I have participated last year in M4 competition. Our approach performed well, finishing as 6th in the competition. This paper in International Journal of Forecasting explains what we used in our approach and why. Here’s the abstract: This paper describes the approach that we implemented for producing the point forecasts and prediction […]
State space ARIMA for supply-chain forecasting
John Boylan and I have been working lately on a paper, explaining the logic behind the ssarima() function from the smooth package. This paper has finally been accepted and published. Also, based on a modified version of the ssarima() function, I have developed a SSARIMA module for Smoothie software, developed by DemandWorks company. Both the […]
Multiplicative State-Space Models for Intermittent Time Series
John Boylan and I have been working on a paper about state-space models for intermittent data. We have had some good progress in that direction and have submitted the paper to IJF. Although it is still under review, we decided to publish the working paper in order to promote the thing. Here’s the abstract: Intermittent […]
Old dog, new tricks: a modelling view of simple moving averages
Fotios Petropoulos and I have recently written a paper about a statistical model, underlying Simple Moving Average. Although we are usually taught in Forecasting courses, that there is no such thing, we found one. We have submitted this paper to International Journal of Production Research, and it has been recently accepted (took us ~4 months). […]