A blog by Rob J Hyndman 

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Posts Tagged ‘references’:

Errors on percentage errors

Published on 16 April 2014

The MAPE (mean absolute per­cent­age error) is a pop­u­lar mea­sure for fore­cast accu­racy and is defined as     where denotes an obser­va­tion and denotes its fore­cast, and the mean is taken over . Arm­strong (1985, p.348) was the first (to my knowl­edge) to point out the asym­me­try of the MAPE say­ing that “it has a bias favor­ing esti­mates that are below the actual values”.

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My forecasting book now on Amazon

Published on 9 April 2014

For all those peo­ple ask­ing me how to obtain a print ver­sion of my book “Fore­cast­ing: prin­ci­ples and prac­tice” with George Athana­sopou­los, you now can. Order on Ama​zon​.com Order on Ama​zon​.co​.uk Order on Ama​zon​.fr The online book will con­tinue to be freely avail­able. The print ver­sion of the book is intended to help fund the devel­op­ment of the OTexts plat­form. The price is US45, 27 or €35. Compare that to195 for my pre­vi­ous fore­cast­ing text­book, 150 for Fildes and Ord, or182 for Gonzalez-​​​​Rivera. No mat­ter how good the books are, the prices are absurdly high. OTexts is intended to be a dif­fer­ent kind of pub­lisher — all our books are online and free, those in print will be rea­son­ably priced. The online ver­sion will con­tinue to be updated reg­u­larly. The print ver­sion is a snap­shot of the online ver­sion today. We will release a new print edi­tion occa­sion­ally, no more than annu­ally and only when the online ver­sion has changed enough to war­rant a new print edi­tion. We are plan­ning an offline elec­tronic ver­sion as well. I’ll announce it here when it is ready.

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Top papers in the International Journal of Forecasting

Published on 4 February 2014

Every year or so, Else­vier asks me to nom­i­nate five Inter­na­tional Jour­nal of Fore­cast­ing papers from the last two years to high­light in their mar­ket­ing mate­ri­als as “Editor’s Choice”. I try to select papers across a broad range of sub­jects, and I take into account cita­tions and down­loads as well as my own impres­sion of the paper. That tends to bias my selec­tion a lit­tle towards older papers as they have had more time to accu­mu­late cita­tions. Here are the papers I chose this morn­ing (in the order they appeared): Diebold and Yil­maz (2012) Bet­ter to give than to receive: Pre­dic­tive direc­tional mea­sure­ment of volatil­ity spillovers. IJF 28(1), 57–66. Loter­man, Brown, Martens, Mues, and Bae­sens (2012) Bench­mark­ing regres­sion algo­rithms for loss given default mod­el­ing. IJF 28(1), 161–170. Soyer and Hog­a­rth (2012) The illu­sion of pre­dictabil­ity: How regres­sion sta­tis­tics mis­lead experts. IJF 28(3), 695–711. Fried­man (2012) Fast sparse regres­sion and clas­si­fi­ca­tion. IJF 28(3), 722–738. Davy­denko and Fildes (2013) Mea­sur­ing fore­cast­ing accu­racy: The case of judg­men­tal adjust­ments to SKU-​​​​level demand fore­casts. IJF 29(3), 510–522. Last time I did this, three of the five papers I chose went on to win awards. (I don’t pick the award win­ners — that’s a mat­ter for the whole edi­to­r­ial board.) On the other hand, I didn’t pick the


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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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Free books on statistical learning

Published on 30 January 2014

Hastie, Tib­shi­rani and Friedman’s Ele­ments of Sta­tis­ti­cal Learn­ing first appeared in 2001 and is already a clas­sic. It is my go-​​​​to book when I need a quick refresher on a machine learn­ing algo­rithm. I like it because it is writ­ten using the lan­guage and per­spec­tive of sta­tis­tics, and pro­vides a very use­ful entry point into the lit­er­a­ture of machine learn­ing which has its own ter­mi­nol­ogy for sta­tis­ti­cal con­cepts. A free down­load­able pdf ver­sion is avail­able on the web­site. Recently, a sim­pler related book appeared enti­tled Intro­duc­tion to Sta­tis­ti­cal Learn­ing with appli­ca­tions in R by James, Wit­ten, Hastie and Tib­shi­rani. It “is aimed for upper level under­grad­u­ate stu­dents, mas­ters stu­dents and Ph.D. stu­dents in the non-​​​​mathematical sci­ences”. This would be a great text­book for our new 3rd year sub­ject on Busi­ness Ana­lyt­ics. The R code is a wel­come addi­tion in show­ing how to imple­ment the meth­ods. Again, a free down­load­able pdf ver­sion is avail­able on the web­site. There is also a new, free book on Sta­tis­ti­cal foun­da­tions of machine learn­ing by Bön­tempi and Ben Taieb avail­able on the OTexts plat­form. This is more of a hand­book and is writ­ten by two authors com­ing from a machine learn­ing back­ground. R code is also pro­vided. Being an OTexts book, it is con­tin­u­ally updated and revised, and is freely avail­able


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OTexts​.org is launched

Published on 27 September 2013

The pub­lish­ing plat­form I set up for my fore­cast­ing book has now been extended to cover more books and greater func­tion­al­ity. Check it out at www​.otexts​.org.

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Managing research ideas

Published on 25 May 2013

I received this email today: Dear Pro­fes­sor Hyn­d­man, I was won­der­ing if you could maybe give me some advice on how to orga­nize your research process. I am able to search the lit­er­a­ture on a cer­tain topic and iden­tify where there is a ques­tion to work with. My main dif­fi­cult is to orga­nize my paper anno­ta­tions in order to help me to guide my research process, i.e, how to man­age the infor­ma­tion gath­ered in those papers to com­pose and struc­ture a doc­u­ment which can rep­re­sent the research devel­oped so far. I have been look­ing at dif­fer­ent tools such scrivener, Qiqqa, papers2, etc but I am not sure if one of these tools would be the right way to go. To be hon­est I am not even sure a tool would do what I am look­ing for, not just orga­nize ref­er­ences and anno­tate pdfs but to get more con­trol of my research process. I appre­ci­ate if I could get your thoughts on this subject.

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Establishing priority

Published on 6 May 2013

The nature of research is that other peo­ple are prob­a­bly work­ing on sim­i­lar ideas to you, and it is pos­si­ble that some­one will beat you to pub­lish­ing them.

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Blogs about research

Published on 9 August 2012

If you find this blog help­ful (or even if you don’t but you’re inter­ested in blogs on research issues and tools), there are a few other blogs about doing research that you might find use­ful. Here are a few that I read. Pat­ter — Pat Thom­son. The The­sis Whis­perer — Inger Mew­burn. The Research Whis­perer – sev­eral RMIT researchers. the (research) supervisor’s friend — Geof Hill. My Research Rants – Jordi Cabot. The Three Month The­sis – James Hay­ton. prof­se­ri­ous – Anthony Finkel­stein. Aca­d­e­mic Life — Mar­i­aluisa Aliotta. Help for New Pro­fes­sors — Faye Hicks. The Art of Sci­en­tific Writ­ing – Faye Hicks. Explo­rations of style– Rachael Cay­ley. shar­manedit — Anna Shar­man. Grad­Hacker – writ­ers from sev­eral uni­ver­si­ties. PhD Life – War­wick Uni stu­dents. PhD Comics — essen­tial read­ing for every PhD stu­dent, and good ther­apy. I’ve cre­ated a bun­dle so you can sub­scribe to all of these in one go. Of course, there are lots of sta­tis­tics blogs as well, and blogs about other research dis­ci­plines. The ones above are those that con­cen­trate on generic research issues.

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Read the literature

Published on 3 August 2012

I’ve just fin­ished another reviewer report for a jour­nal, and yet again I’ve had to make com­ments about read­ing the lit­er­a­ture. It’s not dif­fi­cult. Before you write a paper, read what other peo­ple have done. A sim­ple search on Google scholar will usu­ally do the trick. And before you sub­mit a paper, check again that you haven’t missed any­thing impor­tant. The paper I reviewed today did not cite a sin­gle ref­er­ence from either of the two most active research groups in the area in the last ten years. Any search on the topic would have turned up about a dozen papers from these two groups alone. I don’t mind if papers miss a ref­er­ence or two, espe­cially if they have been pub­lished in an obscure out­let. But I will rec­om­mend a straight reject if a paper hasn’t cited any of the most impor­tant papers from the last five years. Part of a researcher’s task is to engage with what has already been done, and show how any new ideas dif­fer from or extend on pre­vi­ous work.

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