Ivan Svetunkov 
Sometimes I see people referring to a “deterministic” forecast, and I have some personal issues with this. Because if you apply a model to data then there is nothing deterministic about your forecasts! In many contexts, “deterministic” has a precise meaning: no randomness, no uncertainty. A deterministic solution to an optimisation problem (e.g. linear programming) … Read more
Apparently, we need to talk about scaling of error measures because this is not as obvious as it seems. In forecasting literature, since early days of the area, there has been a general consensus that the forecast errors from the individual time series should not be analysed and aggregated as is. This is because you … Read more
Great news, everyone! smooth package for R version 4.4.0 is now on CRAN. Why is this a great news? Let me explain! On this page: What’s new? Evaluation Setup Results What’s next? Here is what’s new since 4.3.0: First, I have worked on tuning the initialisation in adam() in case of backcasting, and improved the … Read more
Here is one small, unexpected piece of news: I now have my first package on PyPI! It’s called fcompdata, and let me tell you a little bit about it. When I test my functions in R, I usually use the M1, M3, and tourism competition datasets because they are diverse enough, containing seasonal, non-seasonal, trended, … Read more
What do you use for model selection? Do you select the best model based on its cross-validated performance, or do you use in-sample measures like AIC? If so, there is a way to improve your selection process further. JORS recently published the paper of Nikos Kourentzes and I based on a simple but powerful idea: … Read more
Teaching statistics as a flipped classroom with the help of AI? You heard that right! That’s exactly what I tried this year – and here are the results. Attached to this post is the student evaluation score for the module. Yes, the number of responses is quite low (only 50% of the cohort), but it … Read more
So, our paper with Anna Sroginis got rejected from a special issue of the International Journal of Production Research after a second round of revision. And here is what I think about this! First things first, why am I writing this post? I want to share failures with the community, because I am tired of … Read more
When I read posts written by some ML experts, I sometimes notice that they either overlook or do not clearly explain a few crucial steps in demand forecasting. In this post, I want to highlight the three most important ones based on my personal experience. First and foremost, stationarity (see the proper definition of the … Read more
Here is another fascinating aspect of the seasonal profile in your data: it can evolve over time due to changing consumer preferences. How so? Let me explain. I’ve worked with a couple of companies where there were some examples of data with drastically changing seasonal patterns over just a few years. For example, Before Covid … Read more
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