Another interesting case in demand forecasting is the high frequency data. For example, if you work with demand on daily level, you might notice that demand increases every Monday but also exhibits proper seasonal fluctuations (e.g. decline every Winter). What do you do in this case? One of the solutions (old but gold) is the […]
We continue our series of posts on the functions from the smooth package for Python/R. Today we will see how to enhance your exponential smoothing with explanatory variables. What? Yes, you heard me! Let’s dive in! We all know that in real life sales don’t just evolve over time on their own. Any univariate model, […]
Last time we saw how to do automated model selection using the ES function from the smooth package. Now I want to show how to produce combined forecasts from ETS. Why bother? There is a vast body of literature on forecast combinations (read this great review). The main idea is that you should not put […]
As some of you have heard, the smooth package is now on PyPI. So, I’ve decided to write a series of posts showcasing how some of its functions work. We start with the basics, ETS. ETS stands for the “Error-Trend-Seasonal” model or ExponenTial Smoothing. It is a statistical model that relies on time series decomposition […]
Here is another piece of news I have been hoping to deliver for quite some time now (since January 2026 actually). We have finally created the first release of the smooth package for Python and it is available on PyPI! Anyone interested? Read more! On this page: Why does “smooth” exist? A bit of history […]
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, […]