Dark themes for writing

I spend much of my day sit­ting in front of a screen, cod­ing or writ­ing. To limit the strain on my eyes, I use a dark theme as much as pos­si­ble. That is, I write with light col­ored text on a dark back­ground. I don’t know why this is not the default in more soft­ware as it makes a big dif­fer­ence after a few hours of writing.

Most of the time, I am writ­ing using either Sub­lime Text, RStu­dio or TeX­studio. Each of them can be set to use a dark theme with syn­tax col­or­ing to high­light struc­tural fea­tures in the text.
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Di Cook is moving to Monash

I’m delighted that Pro­fes­sor Dianne Cook will be join­ing Monash Uni­ver­sity in July 2015 as a Pro­fes­sor of Busi­ness Ana­lyt­ics. Di is an Aus­tralian who has worked in the US for the past 25 years, mostly at Iowa State Uni­ver­sity. She is mov­ing back to Aus­tralia and join­ing the Depart­ment of Econo­met­rics and Busi­ness Sta­tis­tics in the Monash Busi­ness School, as part of our ini­tia­tive in Busi­ness Analytics.

Di is a world leader in data visu­al­iza­tion, and is well-​​​​known for her work on inter­ac­tive graph­ics. She is also the aca­d­e­mic super­vi­sor of sev­eral lead­ing data sci­en­tists includ­ing Hadley Wick­ham and Yihui Xie, both of whom work for RStu­dio.

Di has a great deal of energy and enthu­si­asm for com­pu­ta­tional sta­tis­tics and data visu­al­iza­tion, and will play a key role in devel­op­ing and teach­ing our new sub­jects in busi­ness analytics.

The Monash Busi­ness School is already excep­tion­ally strong in econo­met­rics (ranked 7th in the world on RePEc), and fore­cast­ing (ranked 11th on RePEc), and we have recently expanded into actu­ar­ial sci­ence. With Di join­ing the depart­ment, we will be extend­ing our exper­tise in the area of data visu­al­iza­tion as well.

 

 

New R package for electricity forecasting

Shu Fan and I have devel­oped a model for elec­tric­ity demand fore­cast­ing that is now widely used in Aus­tralia for long-​​term fore­cast­ing of peak elec­tric­ity demand. It has become known as the “Monash Elec­tric­ity Fore­cast­ing Model”. We have decided to release an R pack­age that imple­ments our model so that other peo­ple can eas­ily use it. The pack­age is called “MEFM” and is avail­able on github. We will prob­a­bly also put in on CRAN eventually.

The model was first described in  Hyn­d­man and Fan (2010). We are con­tin­u­ally improv­ing it, and the lat­est ver­sion is decribed in the model doc­u­men­ta­tion which will be updated from time to time.

The pack­age is being released under a GPL licence, so any­one can use it. All we ask is that our work is prop­erly cited.

Nat­u­rally, we are not able to pro­vide free tech­ni­cal sup­port, although we wel­come bug reports. We are avail­able to under­take paid con­sult­ing work in elec­tric­ity forecasting.

 

A time series classification contest

Amongst today’s email was one from some­one run­ning a pri­vate com­pe­ti­tion to clas­sify time series. Here are the essen­tial details.

The data are mea­sure­ments from a med­ical diag­nos­tic machine which takes 1 mea­sure­ment every sec­ond, and after 32–1000 sec­onds, the time series must be clas­si­fied into one of two classes. Some pre-​​classified train­ing data is pro­vided. It is not nec­es­sary to clas­sify all the test data, but you do need to have rel­a­tively high accu­racy on what is clas­si­fied. So you could find a sub­set of more eas­ily clas­si­fi­able test time series, and leave the rest of the test data unclas­si­fied. Con­tinue reading →