Articles by Ivan Svetunkov

How confident are you? Assessing the uncertainty in forecasting

October 18, 2019 | 0 Comments

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_...
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useR!2019 in Toulouse, France

July 11, 2019 | 0 Comments

Salut mes amis! Today I’ve presented my [crayon-5d28070342115118247664-i/] package at the useR!2019 conference in Toulouse, France. This is a nice conference, focused on specific solutions to specific problems. Here, people tend to present functions from their packages (not underlying models, like, for example, at ISF). On one ... [Read more...]

Marketing analytics with greybox

January 7, 2019 | 0 Comments

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 ...
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greybox 0.3.0 – what’s new

August 7, 2018 | 0 Comments

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...
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greybox package for R

May 4, 2018 | 0 Comments

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 ...
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Comparing additive and multiplicative regressions using AIC in R

March 22, 2018 | 0 Comments

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 ... [Read more...]

smooth functions in 2017

January 1, 2018 | 0 Comments

Over the year 2017 the [crayon-5a4afb5f2a18f739005075-i/] 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 ... [Read more...]

«smooth» package for R. Common ground. Part II. Estimators

November 20, 2017 | 0 Comments

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 ... [Read more...]

smooth v2.0.0. What’s new

July 2, 2017 | 0 Comments

Good news, everyone! [crayon-595a10c0c7b68967137438-i/] 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 ... [Read more...]

“smooth” package for R. Common ground. Part I. Prediction intervals

June 11, 2017 | 0 Comments

We have spent previous six posts discussing basics of [crayon-593dadf5825e3102232597-i/] 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 ... [Read more...]

«smooth» package for R. es() function. Part V. Essential parameters

March 4, 2017 | 0 Comments

While the previous posts on [crayon-58bb8f9870904801159552-i/] function contained two parts: theory of ETS and then the implementation – this post will cover only the latter. We won’t discuss anything new, we will mainly look into several parameters that the exponential smoothing function has and what they allow ... [Read more...]

“smooth” package for R. es() function. Part III. Multiplicative models

November 18, 2016 | 0 Comments

Theoretical stuff Last time we talked about pure additive models, today I want to discuss multiplicative ones. There is a general scepticism about pure multiplicative exponential smoothing models in the forecasters society, because it is not clear why level, trend, seasonality and error term should be multiplied. Well, when it ... [Read more...]

“smooth” package for R. es() function. Part II. Pure additive models

November 2, 2016 | 0 Comments

A bit of statistics As mentioned in the previous post, all the details of models underlying functions of “smooth” package can be found in extensive documentation. Here I want to discuss several basic, important aspects of statistical model underlying [crayon-58309f2b41e8b175266536-i/] and how it is implementated ... [Read more...]

“smooth” package for R. es() function. Part I

October 14, 2016 | 0 Comments

Good news, everyone! “smooth” package is now available on CRAN. And it is time to look into what this package can do and why it is needed at all. The package itself contains some documentation that you can use as a starting point. For example, there are vignettes, which show ... [Read more...]
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