Blog Archives

Coherent population forecasting using R

July 23, 2014
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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” forecasts are...

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Plotting the characteristic roots for ARIMA models

July 23, 2014
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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 must be of class Arima or ar") if(class(object) ==...

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Variations on rolling forecasts

July 15, 2014
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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...

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Varian on big data

June 15, 2014
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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 use them,...

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Specifying complicated groups of time series in hts

June 14, 2014
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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 also a separate product hierarchy: product...

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European talks. June-July 2014

June 14, 2014
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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, Italy. 24–25 June. Functional time series with applications in demography. Humboldt University,...

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ARIMA models with long lags

May 7, 2014
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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) with drift as the lowest AICc. However, my boss (who does not use R) takes issue with low-order AR and MA because...

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New jobs in business analytics at Monash

May 4, 2014
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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 positions will continue after...

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Publishing an R package in the Journal of Statistical Software

April 23, 2014
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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...

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Seven forecasting blogs

April 21, 2014
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There are several other blogs on forecasting that readers might be interested in. Here are seven worth following: No Hesitations by Francis Diebold (Professor of Economics, University of Pennsylvania). Diebold needs no introduction to forecasters. He primarily covers forecasting in economics and finance, but also xkcd cartoons, graphics, research issues, etc. Econometrics Beat by Dave Giles. Dave is a professor of...

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