A blog by Rob J Hyndman 

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A new candidate for worst figure

Published on 1 September 2014

Today I read a paper that had been sub­mit­ted to the IJF which included the fol­low­ing figure

worstgraphic

along with sev­eral sim­i­lar plots. (Click for a larger ver­sion.) I haven’t seen any­thing this bad for a long time. In fact, I think I would find it very dif­fi­cult to repro­duce using R, or even Excel (which is par­tic­u­larly adept at bad graphics).

A few years ago I pro­duced “Twenty rules for good graph­ics”. I think I need to add a cou­ple of addi­tional rules:

  • Rep­re­sent time changes using lines.
  • Never use fill pat­terns such as cross-​​hatching.

(My orig­i­nal rule #20 said Avoid pie charts.)

It would have been rel­a­tively sim­ple to show these data as six lines on a plot of GDP against time. That would have made it obvi­ous that the Euro­pean GDP was shrink­ing, the GDP of Asia/​Oceania was increas­ing, while other regions of the world were fairly sta­ble. At least I think that is what is hap­pen­ing, but it is very hard to tell from such graph­i­cal obfuscation.

 
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Forecasting with R in WA

Published on 25 August 2014

On 23–25 Sep­tem­ber, I will be run­ning a 3-​​day work­shop in Perth on “Fore­cast­ing: prin­ci­ples and prac­tice” mostly based on my book of the same name.

Work­shop par­tic­i­pants will be assumed to be famil­iar with basic sta­tis­ti­cal tools such as mul­ti­ple regres­sion, but no knowl­edge of time series or fore­cast­ing will be assumed. Some prior expe­ri­ence in R is highly desirable.

Venue: The Uni­ver­sity Club, Uni­ver­sity of West­ern Aus­tralia, Ned­lands WA.

Day 1:
Fore­cast­ing tools, sea­son­al­ity and trends, expo­nen­tial smoothing.
Day 2:
State space mod­els, sta­tion­ar­ity, trans­for­ma­tions, dif­fer­enc­ing, ARIMA models.
Day 3:
Time series cross-​​validation, dynamic regres­sion, hier­ar­chi­cal fore­cast­ing, non­lin­ear models.

The course will involve a mix­ture of lec­tures and prac­ti­cal ses­sions using R. Each par­tic­i­pant must bring their own lap­top with R installed, along with the fpp pack­age and its dependencies.

For costs and enrol­ment details, go to
http://​www​.cas​.maths​.uwa​.edu​.au/​c​o​u​r​s​e​s​/​f​o​r​e​c​a​sting.

 
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biblatex for statisticians

Published on 22 August 2014

I am now using bibla­tex for all my bib­li­o­graphic work as it seems to have devel­oped enough to be sta­ble and reli­able. The big advan­tage of bibla­tex is that it is easy to for­mat the bib­li­og­ra­phy to con­form to spe­cific jour­nal or pub­lisher styles. It is also pos­si­ble to have struc­tured bib­li­ogra­phies (e.g., divided into sec­tions: books, papers, R pack­ages, etc.) (more…)

 
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GEFCom 2014 energy forecasting competition is underway

Published on 18 August 2014

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​.gef​com​.org.

This time, the com­pe­ti­tion is hosted on Crow­d­AN­A­LYTIX rather than Kag­gle.

High­lights of GEFCom2014:

  • An upgraded edi­tion from GEFCom2012
  • Four tracks: elec­tric load, elec­tric­ity price, wind power and solar power forecasting.
  • Prob­a­bilis­tic fore­cast­ing: con­tes­tants are required to sub­mit 99 quan­tiles for each step through­out the fore­cast horizon.
  • Rolling fore­cast­ing: incre­men­tal data sets are being released on weekly basis to fore­cast the next period of interest.
  • Prizes for win­ning teams and insti­tu­tions: up to 3 teams from each track will be rec­og­nized as the win­ning team; top insti­tu­tions with mul­ti­ple well-​​performing teams will be rec­og­nized as the win­ning institutions.
  • Global par­tic­i­pa­tion: 200+ peo­ple from 40+ coun­tries have already signed up the GEFCom2014 inter­est list.

Tao Hong (the main orga­nizer) has a few tips on his blog that you should read before starting.

 

 
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Visit of Di Cook

Published on 13 August 2014

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. (more…)

 
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What not to say in a job interview

Published on 12 August 2014

I’ve inter­viewed a few peo­ple for jobs at Monash Uni­ver­sity, and there’s always some­one who comes out with some­thing sur­pris­ing. Here are some real exam­ples. (more…)

 
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Minimal reproducible examples

Published on 11 August 2014

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. (more…)

 
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Student forecasting awards from the IIF

Published on 26 July 2014

At the IIF annual board meet­ing last month in Rot­ter­dam, I sug­gested that we pro­vide awards to the top stu­dents study­ing fore­cast­ing at uni­ver­sity level around the world, to the tune of $100 plus IIF mem­ber­ship for a year. I’m delighted that the idea met with enthu­si­asm, and that the awards are now avail­able. Even bet­ter, my sec­ond year fore­cast­ing sub­ject has been approved for an award.

The IIF have agreed to fund awards for 20 fore­cast­ing courses to start with. I believe they have already had sev­eral appli­ca­tions, so any other fore­cast­ing lec­tur­ers out there will need to be quick if they want to be part of it.

 
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Coherent population forecasting using R

Published on 24 July 2014

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 where male and females (or other sub-​​groups) do not diverge over time. (Essen­tially, we require the dif­fer­ence between the groups to be sta­tion­ary.) When we wrote the 2008 paper, we did not know how to con­strain the fore­casts to be coher­ent in a func­tional data con­text and so this was not dis­cussed. My later 2013 paper pro­vided a way of impos­ing coher­ence. This blog post shows how to imple­ment both ideas using R. (more…)

 
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Plotting the characteristic roots for ARIMA models

Published on 23 July 2014

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). (more…)

 
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