In Cairns, Australia, I have presented the topic from Bath — One for all: forecasting intermittent and non-intermittent demand using one model. It was well received although one of the professors did not understand the main point of the presentation and I could not explain him, why this is important and why the proposed approach […]

# Naughty APEs and the quest for the holy grail

Today I want to tell you a story of naughty APEs and the quest for the holy grail in forecasting. The topic has already been known for a while in academia, but is widely ignored by practitioners. APE stands for Absolute Percentage Error and is one of the simplest error measures, which is supposed to […]

# 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(), […]

# “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(), […]

# “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 […]

# 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 […]

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