Three months have passed since the initial release of 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 and : Rolling Origin First of all, function now has its own class
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 mistake was made –
One of the basic things the students are taught in statistics classes is that the comparison of models using information criteria can only be done when the models have the same response variable. This means, for example, that when you have \(\log(y_t)\) and calculate AIC, then this value is not comparable with AIC from a
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 . It is called . It is useful in cases when you think that your exogenous
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 variable for promotions and we
Over the year 2017 the package has grown from v1.6.0 to v2.3.1. Now it is much more mature and has more downloads. It even now has its own hex (thanks to Fotios Petropoulos): A lot of changes happened in 2017, and it is hard to mention all of them, but the major ones are:
A bit about estimates of parameters Hi everyone! Today I want to tell you about parameters estimation of smooth functions. But before going into details, there are several things that I want to note. In this post we will discuss bias, efficiency and consistency of estimates of parameters, so I will use phrases like “efficient
Good news, everyone! package has recently received a major update. The version on CRAN is now v2.0.0. I thought that this is a big deal, so I decided to pause for a moment and explain what has happened, and why this new version is interesting. First of all, there is a new function, ,
We have spent previous six posts discussing basics of function (underlying models and their implementation). Now it is time to move forward. Starting from this post we will discuss common parameters, shared by all the forecasting functions implemented in smooth. This means that the topics that we discuss are not only applicable to ,
Now that we looked into the basics of function, we can discuss how the optimisation mechanism works, how the parameters are restricted and what are the initials values for the parameters in the optimisation of the function. This will be fairly technical post for the researchers who are interested in the inner (darker) parts
