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: […]
Sometimes, when you need to forecast demand, you may notice that the recorded data contains zeroes. There are several possible reasons for this, but today we’ll briefly discuss one of them. Welcome to the world of “intermittent demand”! Intermittent demand is the demand that happens at irregular frequency (Svetunkov & Boylan, 2023). This means you […]
As we discussed last time, there are two possible strategies in forecasting: recursive and direct. The latter aligns with the estimation of a model using a so-called multistep loss function, such as Mean Squared Error for h-steps-ahead forecast (MSEh). But this is not the only loss function that can be efficiently used for model estimation. […]
So far, we’ve discussed forecasts evaluation, focusing on the precision of point forecasts. However, there are many other dimensions in the evaluation that can provide useful information about your model’s performance. One of them is bias, which we’ll explore today. Introduction But before that, why should we bother with bias? Research suggests that bias is […]
What is “forecasting”? Many people will have a ready answer to this question, but I would argue that not many have spent enough time thinking about it. Should we spend a couple of minutes of our time today to do that? Straight to the point: my answer to the question comes to the following definition: […]
One question I received from my LinkedIn followers was how to evaluate forecast accuracy in practice. MAPE is wrong, but it is easy to use. In practice, we want something simple, informative and straightforward, but not all error measures are easy to calculate and interpret. What should we do? Here is my subjective view. Step […]
We continue our discussion of error measures (if you don’t mind). One other thing that you encounter in forecasting experiments is tables containing several error measures (MASE, RMSSE, MAPE, etc.). Have you seen something like this? Well, this does not make sense, and here is why. The idea of reporting several error measures comes from […]
Authors: Ivan Svetunkov, Nikos Kourentzes, Rebecca Killick Journal: Computational Statistics Abstract: Many modern statistical models are used for both insight and prediction when applied to data. When models are used for prediction one should optimise parameters through a prediction error loss function. Estimation methods based on multiple steps ahead forecast errors have been shown to […]
Previously we discussed how to measure accuracy of point forecasts and performance of prediction intervals in different cases. Now we look into the question how to tell the difference between competing forecasting approaches. Let’s imagine the situation, when we have four forecasting methods applied to 100 time series with accuracy measured in terms of RMSSE: […]
If you work in the field of statistics, analytics, data science or forecasting, then you probably have already noticed that some of the instruments that are used in your field are called “methods”, while the others are called “models”. The issue here is that the people, using these terms, usually know the distinction between them, […]