UPDATE: Starting from the v2.5.6 the C parameter has been renamed into B. This is now consistent across all the functions. 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 […]
Seminar and presentation at Bath University
Last week I have visited Bath University, where Dr. Fotios Petropoulos works. He organised a scientific seminar, where I could present my recent research on topic “One for all: forecasting intermittent and non-intermittent demand using one model“. The presentation was well received and rose several interesting questions from the participants of the seminar, which will […]
“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 […]
“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 […]
“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 […]
“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 […]
“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 […]
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 […]
19th IIF Workshop presentation
An IIF workshop “Supply Chain Forecasting for Operations” took place at Lancaster University on 28th and 29th of June. I have given a presentation on a topic that John Boylan and I are currently working on. We suggest a universal statistical model, that allows uniting standard methods of forecasting (for example, for fast moving products) […]
True model
In the modern statistical literature there is a notion of “true model”, by which people usually mean some abstract mathematical model, presumably lying in the core of observed process. Roughly saying, it is implied that data we have has been generated by some big guy with a white beard sitting in mathematical clouds using some […]