Mixed models In the previous posts we have discussed pure additive and pure multiplicative exponential smoothing models. The next logical step would be to discuss mixed models, where some components have additive and the others have multiplicative nature. But we won’t spend much time on them because I personally think that they do not make […]

# “smooth” package for R. es() function. Part III. Multiplicative models

Theoretical stuff Last time we talked about pure additive models, today I want to discuss multiplicative ones. There is a general scepticism about pure multiplicative exponential smoothing models in the forecasters society, because it is not clear why level, trend, seasonality and error term should be multiplied. Well, when it comes to seasonality, then there […]

# “smooth” package for R. es() function. Part II. Pure additive models

A bit of statistics As mentioned in the previous post, all the details of models underlying functions of “smooth” package can be found in extensive documentation. Here I want to discuss several basic, important aspects of statistical model underlying es() and how it is implementated in R. Today we will have a look at basic […]

# “smooth” package for R. es() function. Part I

Good news, everyone! “smooth” package is now available on CRAN. And it is time to look into what this package can do and why it is needed at all. The package itself contains some documentation that you can use as a starting point. For example, there are vignettes, which show included functions and what they […]

# Exporting R tables in LaTeX

Recently I have started using LaTeX for all my documents and presentations. Don’t ask me why, I just like how texts look there rather than in products of Microsoft (and I in general dislike MS… we have a long unpleasant history). So, I sometimes need to export tables from R into LaTeX. These tables can […]

# 19th IIF Workshop presentation

An IIF workshop “Supply Chain Forecasting for Operations” took place at Lancaster University on 28th and 29th of June. I have given a presentation on a topic that John Boylan and I are currently working on. We suggest a universal statistical model, that allows uniting standard methods of forecasting (for example, for fast moving products) […]

# True model

In the modern statistical literature there is a notion of “true model”, by which people usually mean some abstract mathematical model, presumably lying in the core of observed process. Roughly saying, it is implied that data we have has been generated by some big guy with a white beard sitting in mathematical clouds using some […]

# International Symposium on Forecasting 2016

This time I presented a talk on Trace Forecast Likelihood, based on the presentation given at Ghent and in Higher School of Economics earlier this year. Unfortunately, I was in the session of statisticians, who discussed hypothesis testing and weren’t aware of the area of my presentation. As a result, this presentation passed without any […]

# Visit of Stephan Kolassa

This Wednesday Stephan Kolassa (Senior Research Expert at SAP) has visited Lancaster Centre for Forecasting. He gave a couple of very interesting talks and attended the presentation of PhD topics by Ivan Svetunkov, Yves Sagaert and Oliver Schaer (organised by Nikolaos Kourentzes). My topic was “Trace Forecast Likelihood”, some parts of which I have presented […]

# Complex Exponential Smoothing (Working paper)

Some time ago I have published the working paper on Complex Exponential Smoothing on ResearchGate website. This is the paper written by Nikolaos Kourentzes and I in 2015. It explains a new approach in time series modelling and in forecasting, based on a notion of “information potential”. The model, resulting from this idea, allows to […]