A blog by Rob J Hyndman 

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Posts Tagged ‘computing’:


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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Using old versions of R packages

Published on 10 March 2014

I received this email yes­ter­day: I have been using your ‘fore­cast’ pack­age for more than a year now. I was on R ver­sion 2.15 until last week, but I am hav­ing issues with lubri­date pack­age, hence decided to update R ver­sion to R 3.0.1. In our orga­ni­za­tion even get­ting an open source appli­ca­tion require us to go through a whole lot of approval processes. I asked for R 3.0.1, before I get approval for 3.0.1, a new ver­sion of R ( R 3.0.2 ) came out. Unfor­tu­nately for me fore­cast pack­age was built in R3.0.2. Is there any ver­sion of fore­cast pack­age that works in older ver­sion of R(3.0.1). I just don’t want to go through this entire approval war again within the orga­ni­za­tion. Please help if you have any work around for this This is unfor­tu­nately very com­mon. Many cor­po­rate IT envi­ron­ments lock down com­put­ers to such an extent that it crip­ples the use of mod­ern soft­ware like R which is con­tin­u­ously updated. It also affects uni­ver­si­ties (which should know bet­ter) and I am con­stantly try­ing to invent work-​​​​arounds to the con­straints that Monash IT ser­vices place on staff and stu­dent com­put­ers. Here are a few thoughts that might help.

 
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Highlighting the web

Published on 6 March 2014

Users of my new online fore­cast­ing book have asked about hav­ing a facil­ity for per­sonal high­light­ing of selected sec­tions, as stu­dents often do with print books. We have plans to make this a built-​​​​in part of the plat­form, but for now it is pos­si­ble to do it using a sim­ple browser exten­sion. This approach allows any web­site to be high­lighted, so is even more use­ful than if we only had the facil­ity on OTexts​.org. There are sev­eral pos­si­ble tools avail­able. One of the sim­plest tools that allows both high­light­ing and anno­ta­tions is Diigo.

 
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More time series data online

Published on 27 February 2014

Ear­lier this week I had cof­fee with Ben Fulcher who told me about his online col­lec­tion com­pris­ing about 30,000 time series, mostly med­ical series such as ECG mea­sure­ments, mete­o­ro­log­i­cal series, bird­song, etc. There are some finance series, but not many other data from a busi­ness or eco­nomic con­text, although he does include my Time Series Data Library. In addi­tion, he pro­vides Mat­lab code to com­pute a large num­ber of char­ac­ter­is­tics. Any­one want­ing to test time series algo­rithms on a large col­lec­tion of data should take a look. Unfor­tu­nately there is no R code, and no R inter­face for down­load­ing the data.

 
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Hierarchical forecasting with hts v4.0

Published on 12 February 2014

A new ver­sion of my hts pack­age for R is now on CRAN. It was com­pletely re-​​​​written from scratch. Not a sin­gle line of code sur­vived. There are some minor syn­tax changes, but the biggest change is speed and scope. This ver­sion is many times faster than the pre­vi­ous ver­sion and can han­dle hun­dreds of thou­sands of time series with­out complaining.

 
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Automatic time series forecasting in Granada

Published on 31 January 2014

In two weeks I am pre­sent­ing a work­shop at the Uni­ver­sity of Granada (Spain) on Auto­matic Time Series Fore­cast­ing. Unlike most of my talks, this is not intended to be pri­mar­ily about my own research. Rather it is to pro­vide a state-​​​​of-​​​​the-​​​​art overview of the topic (at a level suit­able for Mas­ters stu­dents in Com­puter Sci­ence). I thought I’d pro­vide some his­tor­i­cal per­spec­tive on the devel­op­ment of auto­matic time series fore­cast­ing, plus give some com­ments on the cur­rent best practices.

 
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New in forecast 5.0

Published on 27 January 2014

Last week, ver­sion 5.0 of the fore­cast pack­age for R was released. There are a few new func­tions and changes made to the pack­age, which is why I increased the ver­sion num­ber to 5.0. Thanks to Earo Wang for help­ing with this new version.

 
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Looking for a new post-​​doc

Published on 22 January 2014

We are look­ing for a new post-​​​​doctoral research fel­low to work on the project “Macro­eco­nomic Fore­cast­ing in a Big Data World”.  Details are given at the link below jobs​.monash​.edu​.au/​j​o​b​D​e​t​a​i​l​s​.​a​s​p​?​s​J​o​b​I​D​s​=​5​19824 This is a two year posi­tion, funded by the Aus­tralian Research Coun­cil, and work­ing with me, George Athana­sopou­los, Farshid Vahid and Anas­ta­sios Pana­giotelis. We are look­ing for some­one with a PhD in econo­met­rics, sta­tis­tics or machine learn­ing, who is well-​​​​trained in com­pu­ta­tion­ally inten­sive meth­ods, and who has a back­ground in at least one of time series analy­sis, macro­eco­nomic mod­el­ling, or Bayesian econometrics.

 
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