This time ISF took place in Oxford. I acted as a programme chair of the event and was quite busy with schedule and some other minor organisational things, but I still found time to present something new. Specifically, I talked about one specific part of ADAM, the part implementing ETS+ARIMA. The idea is that the […]
ETS
The first draft of “Forecasting and Analytics with ADAM”
After working on this for more than a year, I have finally prepared the first draft of my online monograph “Forecasting and Analytics with ADAM“. This is a monograph on the model that unites ETS, ARIMA and regression and introduces advanced features in univariate modelling, including: ETS in a new State Space form; ARIMA in […]
The creation of ADAM – next step in statistical forecasting
Good news everyone! The future of statistical forecasting is finally here :). Have you ever struggled with ETS and needed explanatory variables? Have you ever needed to unite ARIMA and ETS? Have you ever needed to deal with all those zeroes in the data? What about the data with multiple seasonalities? All of this and […]
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 […]
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 […]
Multiplicative State-Space Models for Intermittent Time Series
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 […]
“smooth” package for R. es() function. Part VI. Parameters optimisation
UPDATE: Starting from the v2.5.6 the C parameter has been renamed into B. This is now consistent across all the functions. Now that we looked into the basics of es() function, we can discuss how the optimisation mechanism works, how the parameters are restricted and what are the initials values for the parameters in the […]
“smooth” package for R. es() function. Part V. Essential parameters
While the previous posts on es() function contained two parts: theory of ETS and then the implementation – this post will cover only the latter. We won’t discuss anything new, we will mainly look into several parameters that the exponential smoothing function has and what they allow us to do. We start with initialisation of […]
“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 […]