Fotios Petropoulos and I have participated last year in M4 competition. Our approach performed well, finishing as 6th in the competition. This note in International Journal of Forecasting explains what we used in our approach and why. Here’s the abstract: This paper describes the approach that we implemented for producing the point forecasts and prediction […]

# Applied forecasting

# State space ARIMA for supply-chain forecasting

John Boylan and I have been working lately on a paper, explaining the logic behind the ssarima() function from the smooth package. This paper has finally been accepted and published. Also, based on a modified version of the ssarima() function, I have developed a SSARIMA module for Smoothie software, developed by DemandWorks company. Both the […]

# greybox package for R

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 mistake was made – […]

# smooth functions in 2017

Over the year 2017 the smooth 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 mention all of them, but the major ones are: […]

# “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 […]