A blog by Rob J Hyndman 

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Posts Tagged ‘computing’:

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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Data science market places

Published on 26 May 2014

Some new web­sites are being estab­lished offer­ing “mar­ket places” for data sci­ence. Two I’ve come across recently are Experfy and SnapAnalytx.

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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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Generating tables in LaTeX

Published on 15 April 2014

Typ­ing tables in LaTeX can get messy, but there are some good tools to sim­plify the process. One I dis­cov­ered this week is tables​gen​er​a​tor​.com, a web-​​​​based tool for gen­er­at­ing LaTeX tables. It also allows the table to saved in other for­mats includ­ing HTML and Mark­down. The inter­face is sim­ple, but it does most things. For com­pli­cated tables, some addi­tional for­mat­ting may be necessary.

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Getting a LaTeX system set up

Published on 4 April 2014

Today I was teach­ing the hon­ours stu­dents in econo­met­rics and eco­nom­ics about LaTeX. Here are some brief instruc­tions on how to set up a LaTeX sys­tem on dif­fer­ent oper­at­ing systems.

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Fast computation of cross-​​validation in linear models

Published on 17 March 2014

The leave-​​​​one-​​​​out cross-​​​​validation sta­tis­tic is given by     where , are the obser­va­tions, and is the pre­dicted value obtained when the model is esti­mated with the th case deleted. This is also some­times known as the PRESS (Pre­dic­tion Resid­ual Sum of Squares) sta­tis­tic. It turns out that for lin­ear mod­els, we do not actu­ally have to esti­mate the model times, once for each omit­ted case. Instead, CV can be com­puted after esti­mat­ing the model once on the com­plete data set.

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