A blog by Rob J Hyndman 

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Posts Tagged ‘R’:


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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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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ARIMA models with long lags

Published on 8 May 2014

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)[12] with drift as the low­est AICc. How­ever, my boss (who does not use R) takes issue with low-​​​​order AR and MA because “you’re essen­tially using fore­casted data to make your fore­cast.” His mod­els include AR(10) MA(12)s etc. rather fre­quently. I argue that’s over­fit­ting. I don’t see a great deal of dis­cus­sion in text­books about this, and I’ve never seen such higher-​​​​order mod­els in a text­book set­ting. But are they fairly com­mon in prac­tice? What con­cerns could I raise with him about higher-​​​​order mod­els? Any advice you could give would be appreciated.

 
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New jobs in business analytics at Monash

Published on 4 May 2014

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 that if we can secure suit­able funding.

 
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Publishing an R package in the Journal of Statistical Software

Published on 24 April 2014

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.

 
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Seven forecasting blogs

Published on 22 April 2014

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 eco­nom­ics at the Uni­ver­sity of Vic­to­ria (Canada), for­merly from my own depart­ment at Monash Uni­ver­sity (Aus­tralia), and a native New Zealan­der. Not a lot on fore­cast­ing, but plenty of inter­est­ing posts about econo­met­rics and sta­tis­tics more gen­er­ally. Busi­ness fore­cast­ing by Clive Jones (a pro­fes­sional fore­caster based in Col­orado, USA). Orig­i­nally about sales and new prod­uct fore­cast­ing, but he now cov­ers a lot of other fore­cast­ing top­ics and has an inter­est­ing prac­ti­tioner per­spec­tive. Freakono­met­rics: by Arthur Char­p­en­tier (an actu­ary and pro­fes­sor of math­e­mat­ics at the Uni­ver­sity of Que­bec at Mon­tréal, Canada). This is the most pro­lific blog on this list. Wide rang­ing and tak­ing in sta­tis­tics, fore­cast­ing, econo­met­rics, actu­ar­ial sci­ence, R, and any­thing else that takes his fancy. Some­times in French. No free hunch: the kag­gle blog. Some of the most inter­est­ing posts are from kag­gle com­pe­ti­tion win­ners explain­ing their meth­ods. Energy fore­cast­ing by Tao Hong (for­merly an energy fore­caster for

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