Publications by Ivan Svetunkov
This is just a test page for forecastersblog.org
This is just a test page for forecastersblog.org. Ignore it, please. es() allows selecting between AIC (Akaike Information Criterion), AICc (Akaike Information Criterion corrected) and BIC (Bayesian Information Criterion, also known as Schwarz IC). The very basic information criterion is AIC. It is calculated for a chosen model using formula: \be...
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Exporting R tables in LaTeX
Recently I have started using LaTeX for all my documents and presentations. Don’t ask me why, I just like how texts look there rather than in products of Microsoft (and I in general dislike MS… we have a long unpleasant history). So, I sometimes need to export tables from R into LaTeX. These tables can be huge, so exporting them manually is n...
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“smooth” package for R. es() function. Part I
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 included functions and what they allow to do, but do not go into det...
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“smooth” package for R. es() function. Part II. Pure additive models
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 es() and how it is implementated in R. Today we will have a look at basic pure additive model...
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“smooth” package for R. es() function. Part III. Multiplicative models
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 comes to seasonalit...
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“smooth” package for R. es() function. Part IV. Model selection and combination of forecasts
Mixed models In the previous posts we have discussed pure additive and pure multiplicative exponential smoothing models. The next logical step would be to discuss mixed models, where some components have additive and the others have multiplicative nature. But we won’t spend much time on them because I personally think that they do not make muc...
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«smooth» package for R. es() function. Part V. Essential parameters
While the previous posts on es() 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 us to do. We start with initialisation of es(). History of e...
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«smooth» package for R. es() function. Part VI. Parameters optimisation
Now that we looked into the basics of es() 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 of es(). NOTE. In...
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“smooth” package for R. Common ground. Part I. Prediction intervals
We have spent previous six posts discussing basics of es() 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 es(), but ...
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smooth v2.0.0. What’s new
Good news, everyone! smooth 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, ves(), that implements Vector Exponential Smoothing model....
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