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«smooth» package for R. es() function. Part VI. Parameters optimisation

April 29, 2017
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«smooth» package for R. es() function. Part VI. Parameters optimisation

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

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

March 4, 2017
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«smooth» package for R. es() function. Part V. Essential parameters

While the previous posts on 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

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“smooth” package for R. es() function. Part IV. Model selection and combination of forecasts

January 24, 2017
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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

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

November 18, 2016
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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 seasonality, then there

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

November 2, 2016
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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 and how it is implementated in R. Today we will have a look at basic

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

October 14, 2016
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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

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Exporting R tables in LaTeX

October 12, 2016
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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

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This is just a test page for forecastersblog.org

January 28, 2016
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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: \begin{equation} \label{eq:AIC} \text{AIC} = -2

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