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. […]
error measures
Don’t forget about bias!
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 […]
Best practice for forecasts evaluation for business
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 […]
Avoid using MAPE!
Frankly speaking, I didn’t see the point in discussing MAPE when I wrote recent posts on error measures. However, I’ve received several comments and messages from data scientists and demand planners asking for clarification. So, here it is. TL;DR: Avoid using MAPE! MAPE, or Mean Absolute Percentage Error, is a still-very-popular-in-practice error measure, which is […]
Stop reporting several error measures just for the sake of them!
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 […]
What does “lower error measure” really mean?
“My amazing forecasting method has a lower MASE than any other method!” You’ve probably seen claims like this on social media or in papers. But have you ever thought about what it really means? Many forecasting experiments come to applying several approaches to a dataset, calculating error measures for each method per time series and […]
Error Measures Flow Chart
In order to help master students of Lancaster University Managemen Science department, I have developed a flow chart, that acts as a basic guide on what error measures to use in different circumstances. This is not a complete and far from perfect flow chart, and it assumes that the decision maker knows what intermittent demand […]
Accuracy of forecasting methods: Can you tell the difference?
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: […]
What about all those zeroes? Measuring performance of models on intermittent demand
In one of the previous posts, we have discussed how to measure the accuracy of forecasting methods on the continuous data. All these MAE, RMSE, MASE, RMSSE, rMAE, rRMSE and other measures can give you an information about the mean or median performance of forecasting methods. We have also discussed how to measure the performance […]
How confident are you? Assessing the uncertainty in forecasting
Introduction Some people think that the main idea of forecasting is in predicting the future as accurately as possible. I have bad news for them. The main idea of forecasting is in decreasing the uncertainty. Think about it: any event that we want to predict has some systematic components \(\mu_t\), which could potentially be captured […]