# OR60 presentation. Forecasting using exponential smoothing: the past, the present, the future

Robert Fildes asked me to prepare a review of exponential smoothing for OR60. I thought that it would be boring just to look in the past, so I decided to do past + present + future, adding a model that Nikos and I have started working on some time ago (GUM – Generalised Univariate Model). […]

# «smooth» package for R. Intermittent state-space model. Part I. Introducing the model

One of the features of functions of smooth package is the ability to work with intermittent data and the data with periodically occurring zeroes. Intermittent time series is a series that has non-zero values occurring at irregular frequency (Svetuknov and Boylan, 2017). Imagine retailer who sells green lipsticks. The demand on such a product will […]

# greybox 0.3.0 – what’s new

Three months have passed since the initial release of greybox on CRAN. I would not say that the package develops like crazy, but there have been some changes since May. Let’s have a look. We start by loading both greybox and smooth:

Rolling Origin First of all, ro() function now has its own class […]

# International Symposium on Forecasting 2018

This year I have presented an extension of the research from ISF2017, called “Forecasting intermittent data with complex patterns”. This time we developed the model with “logistic probability”, which allows capturing complex patterns in demand occurrence part of the data. I also tried making the presentation more entertaining and easier to understand by a wider […]

# Presentation at ISMS2018

This year I participated the Informs Society for Marketing Science Conference in Philadelphia. I presented a research that I work on together with Victoria Grigorieva, Yana Salihova and Florian Dost. This is an ongoing research, and we are trying to capture the dynamics of ecosystems on the market of mobile devices in order to see, […]

# 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 – […]

# Comparing additive and multiplicative regressions using AIC in R

One of the basic things the students are taught in statistics classes is that the comparison of models using information criteria can only be done when the models have the same response variable. This means, for example, that when you have $$\log(y_t)$$ and calculate AIC, then this value is not comparable with AIC from a […]

# «smooth» package for R. Common ground. Part IV. Exogenous variables. Advanced stuff

Previously we’ve covered the basics of exogenous variables in smooth functions. Today we will go slightly crazy and discuss automatic variables selection. But before we do that, we need to look at a Santa’s little helper function implemented in smooth. It is called xregExpander(). It is useful in cases when you think that your exogenous […]

# «smooth» package for R. Common ground. Part III. Exogenous variables. Basic stuff

One of the features of the functions in smooth package is the ability to use exogenous (aka “external”) variables. This potentially leads to the increase in the forecasting accuracy (given that you have a good estimate of the future exogenous variable). For example, in retail this can be a binary variable for promotions and we […]

# 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: […]