So, you know quite well that the normal distribution is one of the most popular distributions in statistics. The reasons are manifold, including convenience for the academic community and the fact that it is taught in every single statistics course in the world. But what if we don’t want to be normal? There are situations […]
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 […]
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) […]
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: […]
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 […]
A couple of days ago, I noticed a link to the following paper in a post by Jack Rodenberg: https://arxiv.org/abs/2506.05941v1. The topic seemed interesting and relevant to my work, so I read it, only to find that the paper contains several serious flaws that compromise its findings. Let me explain. Introduction But first, why am […]
Are there any PhD students in the crowd who want to learn more about forecasting? What about academic supervisors who have such students? Show me your hands! This post is for you! This September, we (Lancaster Centre for Marketing Analytics and Forecasting members) will deliver the Natcor course on Forecasting and Predictive Analytics at Lancaster […]