John Boylan and I have been working on a paper about state-space models for intermittent data. We have had some good progress in that direction and have submitted the paper to IJF. Although it is still under review, we decided to publish the working paper in order to promote the thing. Here’s the abstract: Intermittent […]
statistics
Old dog, new tricks: a modelling view of simple moving averages
Fotios Petropoulos and I have recently written a paper about a statistical model, underlying Simple Moving Average. Although we are usually taught in Forecasting courses, that there is no such thing, we found one. We have submitted this paper to International Journal of Production Research, and it has been recently accepted (took us ~4 months). […]
“smooth” package for R. es() function. Part IV. Model selection and combination of forecasts
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 […]
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 […]
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 […]