Everything is random! Your data, your model, its parameter estimates, the forecasts it produces, and even the minimum of the loss function you used. There is no such thing as a “deterministic” forecast – everything is stochastic! Whenever you work with data, you are working with a sample from a population. In some cases, this […]
There are many different issues with capturing seasonality in time series. In this short post, I’d like to discuss one of the most annoying ones. I’m talking about the seasonal pattern that shifts over time. What I mean is that, for example, instead of having the standard number of observations in the cycle (e.g., 24 […]
In forecasting, the term seasonality doesn’t always mean what you think it does. It encompasses more than just patterns repeating from one season to the next. In fact, seasonality covers a wide range of periodic behaviors, and can have some issues associated with the naming conventions. Should we discuss? First things first: when we say […]
If someone tells you that method X solves all problems and is the best one ever, they are either lying intentionally or do not fully understand what they are talking about. There is no such thing as “the best approach for everything”. Let me explain. Consider two products sold by retailers: ice cream and bread. […]
Image above depicts a fashion model making a presentation about a forecasting method. I like the forecast for the final period in that image… Over the last few years, I’ve seen phrases like “LightGBM model” or “Neural Network model” on LinkedIn many times, and the statistician in me shivers every time. So, I figured it’s […]
One thing that bothers me when I read posts on social media or papers in peer-reviewed journals is the claim that a proposed approach is “assumption-free.” In forecasting, this is never true. Such an approach is like a spherical unicorn in a vacuum (see image above). Here’s why. Every model is a simplification of reality, […]
Christmas and the New Year are upon us, and I wanted to publish a celebratory post before taking a break. Instead of writing something educational, I decided to simply recommend a paper for you to read over the holidays – something you might have overlooked in the past couple of years. Here it is or […]
I’ve seen several times ML experts applying principles of classification for intermittent demand forecasting. For example, they try predicting, WHEN the demand will happen. This is not a very sensible thing to do. The featured image in this post shows two forecasting approaches: one that tries to predict when demand happens (the yellow line), and […]
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 […]
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 […]