Blog Archives

GEFCom 2014 energy forecasting competition is underway

August 17, 2014
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GEFCom 2014 is the most advanced energy forecasting competition ever organized, both in terms of the data involved, and in terms of the way the forecasts will be evaluated. So everyone interested in energy forecasting should head over to the competition webpage and start forecasting: www.gefcom.org. This time, the competition is hosted on CrowdANALYTIX rather than Kaggle. Highlights of GEFCom2014: An...

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Visit of Di Cook

August 12, 2014
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Visit of Di Cook

Next week, Professor Di Cook from Iowa State University is visiting my research group at Monash University. Di is a world leader in data visualization, and is especially well-known for her work on interactive graphics and the XGobi and GGobi software. See her book with Deb Swayne for details. For those wanting to hear her speak, read on. Research seminar She...

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Minimal reproducible examples

August 10, 2014
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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,...

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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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