International Symposium on Forecasting 2019

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 […]

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 […]

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 […]