Is there such thing as “Time series forecasting”? I personally don’t like this term and think that we should use a different one. Which one? Come with me in this post to find out. I understand why people use the term “Time series forecasting” – they want to show the type of data they work […]
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). […]
Another question my students sometimes ask is how to define the sizes for the training and test sets in a forecasting experiment. If you’ve done data mining or machine learning, you’re likely familiar with this concept. But when it comes to forecasting, there are a few nuances. Let’s discuss. First and foremost, in forecasting, the […]
One of the questions my students sometimes ask is how to set the forecast horizon. The answer depends largely on the task at hand, but there are still some guidelines. First, the forecast horizon depends on data granularity. A “year ahead” forecast on monthly data means forecasting 12 steps ahead, while for daily data, it […]
Look at the image above. Which forecast seems more appropriate: the red straight line (1) or the purple wavy line (2)? Many demand planners might choose option 2, thinking it better captures the ups and downs. But, in many cases, the straight line is just fine. Here’s why. In a previous post on Structure vs. […]
You’ve probably heard the phrase “all forecasts are wrong”, suggesting that the future is unpredictable and that no forecast will ever match the actual outcome. Well, this phrase is not entirely correct, and here’s why. When your favourite forecasting approach generates point forecasts, it usually provides a conditional mean. This means it’s giving you the […]
One of the core ideas in statistics, which extends to many other fields including forecasting, is the concept of structure versus noise. You’ve probably heard of it, but it’s often overlooked by those without a strong quantitative background. So, let’s discuss. The core of the idea is that any data consists of two fundamental parts: […]
Back in 2022, my father asked me to help him in amending and editing a monograph he wrote on the topic of “Complex-Valued Econometrics”. The original book focused on dynamic models, but after looking through the material and a thorough discussion, we decided to write something more fundamental. The monograph is based on the research […]
I have summarised several posts on point forecasts evaluation in an article for the Foresight journal. Mike Gilliland, being the Editor-in-Chief of the journal, contributed to the paper a lot, making it read much smoother, but preferred not to be included as the co-author. This article was recently published in the issue 74 for Q3:2024. […]
When you start working with your data and suddenly realise that there are zeroes there, i.e. it is intermittent demand, what should you do first? Some people use SBC classification, but is that what you need? Let’s discuss! Intermittent demand comes in different flavours: sometimes zeroes occur frequently with low demand volumes, while other times […]