The ISF2019 took place in Thessaloniki, Greece. This time I presented a spin-off of my research on intermittent demand in retail, entitled as “What about those sweet melons? Using mixture models for demand forecasting in retail”. The idea is quite trivial and simple: use mixture distribution regressions (e.g. logistic and log-normal distributions) in order to […]

# Univariate models

# SMUG2019

I was recently invited to attend the SMUG2019 conference (SMoothie Users Group), organised by Demand Works company in New York. They asked me to present two topics: State space ARIMA for Supply Chain Forecasting, based on which I have developed a module for Smoothie a couple of years ago, Artificial Intelligence in Business, one of […]

# A simple combination of univariate models

Fotios Petropoulos and I have participated last year in M4 competition. Our approach performed well, finishing as 6th in the competition. This paper 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 […]

# 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 […]

# 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

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 […]

# 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: library(greybox) library(smooth) Rolling Origin First of all, ro() function now has its own […]

# 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 […]

# 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 […]