Univariate models

Multiplicative State-Space Models for Intermittent Time Series

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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 … Read more

“smooth” package for R. Common ground. Part I. Prediction intervals

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UPDATE: Starting from v2.5.1 the parameter intervals has been renamed into interval for the consistency purposes with the other R functions. We have spent previous six posts discussing basics of es() function (underlying models and their implementation). Now it is time to move forward. Starting from this post we will discuss common parameters, shared by … Read more

“smooth” package for R. es() function. Part VI. Parameters optimisation

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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 … Read more

“smooth” package for R. es() function. Part V. Essential parameters

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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 … Read more

“smooth” package for R. es() function. Part IV. Model selection and combination of forecasts

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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 … Read more

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

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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 … Read more

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

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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 … Read more

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

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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 … Read more

Complex Exponential Smoothing (Working paper)

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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 … Read more