A blog by Rob J Hyndman 

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Posts Tagged ‘forecasting’:

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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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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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.

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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).

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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.

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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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Varian on big data

Published on 16 June 2014

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 entrée to the LASSO and the elas­tic net, and to slab and spike regres­sion. Not enough to be able to use them, but ok if you’ve no idea what they are.

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Specifying complicated groups of time series in hts

Published on 15 June 2014

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 groups, prod­uct types, packet size. Fore­casts of all the dif­fer­ent types of aggre­ga­tion are required; e.g., prod­uct type A within region X. The aggre­ga­tion struc­ture is a cross-​​​​product of the two hier­ar­chies. This frame­work includes even appar­ently non-​​​​hierarchical data: con­sider the sim­ple case of a time series of deaths split by sex and state. We can con­sider sex and state as two very sim­ple hier­ar­chies with only one level each. Then we wish to fore­cast the aggre­gates of all com­bi­na­tions of the two hier­ar­chies. Any num­ber of sep­a­rate hier­ar­chies can be com­bined in this way. Non-​​​​hierarchical fac­tors such as sex can be treated as single-​​​​level hierarchies.

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European talks. June-​​July 2014

Published on 14 June 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, Italy. 24–25 June. Func­tional time series with appli­ca­tions in demog­ra­phy. Hum­boldt Uni­ver­sity, Berlin. 1 July. Fast com­pu­ta­tion of rec­on­ciled fore­casts in hier­ar­chi­cal and grouped time series. Inter­na­tional Sym­po­sium on Fore­cast­ing, Rot­ter­dam, Netherlands.

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