UPDATE: Starting from smooth v 2.5.0, the model and the respective functions have changed. Now instead of calling the parameter intermittent and working with iss(), one needs to use occurrence and oes() respectively. This post has been updated on 25 April 2019. One of the features of functions of smooth package is the ability to […]

# Author: Ivan Svetunkov

# 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:

1 2 |
library(greybox) library(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: […]

# Conferences 2018

Next year I’m going to attend several forecasting-related conferences. I’ve decided to make a list in order not to forget what to attend. Here it is: International Symposium on Forecasting, 17-20 June, 2018, Boulder, Colorado, USA. This is the key event in forecasting, which any self-respecting forecaster should not miss. I don’t know what to […]