This year, I couldn’t attend the International Symposium on Forecasting (organised by the International Institute of Forecasters), which I usually do, so instead I went to Gran Canaria for the International Conference on Time Series and Forecasting (aka ITISE). The location was fantastic, and I enjoyed several talks. I was also glad to catch up … Read more
Good news! The smooth package v4.3.0 is now on CRAN. And there are several things worth mentioning, so I have written this post. New default initialisation mechanism Since the beginning of the package, the smooth functions supported three ways for initialising the state vector (the vector that includes level, trend, seasonal indices): optimisation, backcasting and … Read more
Here is one thing you have probably not heard of: a workshop on Open Source Forecasting software, held in Beijing on 26th – 27th June 2025. This was a closed event, with speakers attending by invitation only. It focused on recent advancements and potential avenues in open-source forecasting software. But why am I writing about … Read more
I have been asked recently by a colleague of mine how to extract the variance from a model estimated using adam() function from the smooth package in R. The problem was that that person started reading the source code of the forecast.adam() and got lost between the lines (this happens to me as well sometimes). … Read more
Back in 2015, when I was working on my paper on Complex Exponential Smoothing, I conducted a simple simulation experiment to check how ARIMA and ETS select components/orders in time series. And I found something interesting… One of the important steps in forecasting with statistical models is identifying the existing structure. In the case of … Read more
“My amazing forecasting method has a lower MASE than any other method!” You’ve probably seen claims like this on social media or in papers. But have you ever thought about what it really means? Many forecasting experiments come to applying several approaches to a dataset, calculating error measures for each method per time series and … Read more
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 … Read more
On 15th December 2023, I presented in a CMAF Friday Forecasting Talks webinar on the topic of “Why you should care about exponential smoothing”. The motivation was to give a fresh view on the good old model and show how it started, how it evolved over time and how it can be improved. With this … Read more
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 … Read more
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 … Read more