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 ...

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_...

Two years ago I have written a post “Naughty APEs and the quest for the holy grail“, where I have discussed why percentage-based error measures (such as MPE, MAPE, sMAPE) are not good for the task of forecasting performance evaluation. However, it seems to me that I did not explain ...

One of the reasons why I have started the [crayon-5c3391557694e638764676-i/] package is to use it for marketing research and marketing analytics. The common problem that I face, when working with these courses is analysing the data measured in different scales. While R handles numeric scales natively, the ...

Intro One of the features of functions of smooth package is the ability to work with intermittent data and the data with periodically occurring zeroes. Intermittent time series is a series that has non-zero values occurring at irregular frequency (Svetuknov and Boylan, 2017). Imagine retailer who sells green leap sticks. The ...

Three months have passed since the initial release of [crayon-5b6a25a3704bb539673860-i/] on CRAN. I would not say that the package develops like crazy, but there have been some changes since May. Let’s have a look. We start by loading both [crayon-5b6a25a3704ca424011901...

I am delighted to announce a new package on CRAN. It is called “greybox”. I know, what my American friends will say, as soon as they see the name – they will claim that there is a typo, and that it should be “a” instead of “e”. But in fact no ...

Previously we’ve covered the basics of exogenous variables in smooth functions. Today we will go slightly crazy and discuss automatic variables selection. But before we do that, we need to look at a Santa’s little helper function implemented in [crayon-5a7f874ccc437104037960-i/]. It is called [crayon-5...

One of the features of the functions in smooth package is the ability to use exogenous (aka “external”) variables. This potentially leads to the increase in the forecasting accuracy (given that you have a good estimate of the future exogenous variable). For example, in retail this can be a binary ...