Two years ago I have written a post “Naughty APEs and the quest for the holy grail“, where I have discussed why percentage-based error measures (such as MPE, MAPE, sMAPE) are not good for the task of forecasting performance evaluation. However, it seems to me that I did not explain the topic to the full […]
Author: Ivan Svetunkov
useR!2019 in Toulouse, France
Salut mes amis! Today I’ve presented my smooth package at the useR!2019 conference in Toulouse, France. This is a nice conference, focused on specific solutions to specific problems. Here, people tend to present functions from their packages (not underlying models, like, for example, at ISF). On one hand, this has its own limitations, but on […]
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 […]
Conferences 2019
I usually prepare these types of posts at the end of the previous year, but this time I failed to do that earlier. As a result many deadlines for the abstract submissions have already passed. However, there are still several events that you can register for and attend in 2019: 5th April, CMAF Workshop on […]
Analytics with greybox
One of the reasons why I have started the greybox package is to use it for marketing research and marketing analytics. The common problem that I face, when working with these courses is analysing the data measured in different scales. While R handles numeric scales natively, the work with categorical is not satisfactory. Yes, I […]
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 3.0.0, the occurrence part of the model has been removed from es() and other functions. The only one that implements this now is adam(). This post has been updated on 01 January 2021. UPDATE: Starting from smooth v 2.5.0, the model and the respective functions have changed. Now instead of […]