Here is another piece of news I have been hoping to deliver for quite some time now. We have finally created the first release of the smooth package for Python. Anyone interested? Read more! On this page: Why does “smooth” exist? A bit of history How to install What works An example Evaluation Setup Jupiter […]
Many of you have seen this image on the Internet — I’ve seen it myself a few times on LinkedIn lately. People say it depicts the “Dunning-Kruger” effect… But did you know this is actually an internet meme with little to do with the original paper? Here is one of the recent examples, a screenshot […]
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) […]
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 […]
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 […]
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, […]
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: […]
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 […]
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 […]
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 […]