Publications by Rob J Hyndman
Publishing an R package in the Journal of Statistical Software
I’ve been an editor of JSS for the last few years, and as a result I tend to get email from people asking me about publishing papers describing R packages in JSS. So for all those wondering, here are some general comments. JSS prefers to publish papers about packages where the package is on CRAN and has been there long enough to have matured (i...
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New jobs in business analytics at Monash
We have an exciting new initiative at Monash University with some new positions in business analytics. This is part of a plan to strengthen our research and teaching in the data science/computational statistics area. We are hoping to make multiple appointments, at junior and senior levels. These are five-year appointments, but we hope that the po...
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ARIMA models with long lags
Today’s email question: I work within a government budget office and sometimes have to forecast fairly simple time series several quarters into the future. Auto.arima() works great and I often get something along the lines of: ARIMA(0,0,1)(1,1,0)[12] with drift as the lowest AICc. However, my boss (who does not use R) takes issue with low-orde...
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European talks. June-July 2014
For the next month I am travelling in Europe and will be giving the following talks. 17 June. Challenges in forecasting peak electricity demand. Energy Forum, Sierre, Valais/Wallis, Switzerland. 20 June. Common functional principal component models for mortality forecasting. International Workshop on Functional and Operatorial Statistics. Stresa,...
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Specifying complicated groups of time series in hts
With the latest version of the hts package for R, it is now possible to specify rather complicated grouping structures relatively easily. All aggregation structures can be represented as hierarchies or as cross-products of hierarchies. For example, a hierarchical time series may be based on geography: country, state, region, store. Often there is...
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Varian on big data
Last week my research group discussed Hal Varian’s interesting new paper on “Big data: new tricks for econometrics”, Journal of Economic Perspectives, 28(2): 3–28. It’s a nice introduction to trees, bagging and forests, plus a very brief entree to the LASSO and the elastic net, and to slab and spike regression. Not enough to be able to ...
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Variations on rolling forecasts
Rolling forecasts are commonly used to compare time series models. Here are a few of the ways they can be computed using R. I will use ARIMA models as a vehicle of illustration, but the code can easily be adapted to other univariate time series models. One-step forecasts without re-estimation The simplest approach is to estimate the model on a si...
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Plotting the characteristic roots for ARIMA models
When modelling data with ARIMA models, it is sometimes useful to plot the inverse characteristic roots. The following functions will compute and plot the inverse roots for any fitted ARIMA model (including seasonal models). # Compute AR roots arroots <- function(object) { if(class(object) != "Arima" & class(object) != "ar") stop("object mus...
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Coherent population forecasting using R
This is an example of how to use the demography package in R for stochastic population forecasting with coherent components. It is based on the papers by Hyndman and Booth (IJF 2008) and Hyndman, Booth and Yasmeen (Demography 2013). I will use Australian data from 1950 to 2009 and forecast the next 50 years. In demography, “coherent” forecast...
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Minimal reproducible examples
I occasionally get emails from people thinking they have found a bug in one of my R packages, and I usually have to reply asking them to provide a minimal reproducible example (MRE). This post is to provide instructions on how to create a MRE. Bug reports on github, not email First, if you think there is a bug, please don’t send me emails. Inst...
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