A blog by Rob J Hyndman 

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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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I am not an econometrician

Published on 21 July 2014

I am a sta­tis­ti­cian, but I have worked in a depart­ment of pre­dom­i­nantly econo­me­tri­cians for the past 17 years. It is a lit­tle like an Aus­tralian vis­it­ing the United States. Ini­tially, it seems that we talk the same lan­guage, do the same sorts of things, and have a very sim­i­lar cul­ture. But the longer you stay there, the more you realise there are dif­fer­ences that run deep and affect the way you see the world.

Last week at my research group meet­ing, I spoke about some of the dif­fer­ences I have noticed. Coin­ci­den­tally, Andrew Gel­man blogged about the same issue a day later. (more…)

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Variations on rolling forecasts

Published on 16 July 2014

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. (more…)

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SAS/​IIF grants

Published on 15 July 2014

Every year, the Inter­na­tional Insti­tute of Fore­cast­ers in con­junc­tion with SAS offer some small grants to help pro­mote research in fore­cast­ing. There are two $5000 grants per year for research on fore­cast­ing method­ol­ogy and appli­ca­tions. This year, appli­ca­tions close on 30 Sep­tem­ber 2014. More details are given here.

Infor­ma­tion about past SAS-​​IIF awards is given on the IIF web­site. It is inter­est­ing to see the range of top­ics cov­ered. Here are the win­ning projects in the last two years:

  • Jef­frey Stone­braker: “Prob­a­bilis­tic Fore­cast­ing of the Global Demand for the Treat­ment of Hemo­philia B.”
  • Yongchen (Her­bert) Zhao: “Robust Real-​​Time Auto­mated Fore­cast Com­bi­na­tion in SAS: Devel­op­ment of a SAS Pro­ce­dure and a Com­pre­hen­sive Eval­u­a­tion of Recently Devel­oped Com­bi­na­tion Methods.”
  • Zoe Theocharis, Nigel Har­vey, Leonard Smith: “Improv­ing judg­men­tal input to hur­ri­cane fore­casts in the insur­ance and rein­sur­ance sector.”
  • Elena-​​Ivona Dumitrescu, Janine Chris­tine Bal­ter, Peter Rein­hard Hansen: “Fore­cast­ing Exchange Rate Volatil­ity: Mul­ti­vari­ate Real­ized GARCH Framework.”
  • Yorghos Tripodis: “Fore­cast­ing the Cog­ni­tive Sta­tus in an Aging Population.”
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