A blog by Rob J Hyndman 

Twitter Gplus RSS

Posts Tagged ‘teaching’:

Forecasting with R in WA

Published on 25 August 2014

On 23–25 Sep­tem­ber, I will be run­ning a 3-​​​​day work­shop in Perth on “Fore­cast­ing: prin­ci­ples and prac­tice” mostly based on my book of the same name. Work­shop par­tic­i­pants will be assumed to be famil­iar with basic sta­tis­ti­cal tools such as mul­ti­ple regres­sion, but no knowl­edge of time series or fore­cast­ing will be assumed. Some prior expe­ri­ence in R is highly desir­able. Venue: The Uni­ver­sity Club, Uni­ver­sity of West­ern Aus­tralia, Ned­lands WA. Day 1: Fore­cast­ing tools, sea­son­al­ity and trends, expo­nen­tial smooth­ing. Day 2: State space mod­els, sta­tion­ar­ity, trans­for­ma­tions, dif­fer­enc­ing, ARIMA mod­els. Day 3: Time series cross-​​​​validation, dynamic regres­sion, hier­ar­chi­cal fore­cast­ing, non­lin­ear mod­els. The course will involve a mix­ture of lec­tures and prac­ti­cal ses­sions using R. Each par­tic­i­pant must bring their own lap­top with R installed, along with the fpp pack­age and its depen­den­cies. For costs and enrol­ment details, go to http://​www​.cas​.maths​.uwa​.edu​.au/​c​o​u​r​s​e​s​/​f​o​r​e​c​a​sting.

No Comments  comments 

Student forecasting awards from the IIF

Published on 26 July 2014

At the IIF annual board meet­ing last month in Rot­ter­dam, I sug­gested that we pro­vide awards to the top stu­dents study­ing fore­cast­ing at uni­ver­sity level around the world, to the tune of $100 plus IIF mem­ber­ship for a year. I’m delighted that the idea met with enthu­si­asm, and that the awards are now avail­able. Even bet­ter, my sec­ond year fore­cast­ing sub­ject has been approved for an award. The IIF have agreed to fund awards for 20 fore­cast­ing courses to start with. I believe they have already had sev­eral appli­ca­tions, so any other fore­cast­ing lec­tur­ers out there will need to be quick if they want to be part of it.

No Comments  comments 

Creating a handout from beamer slides

Published on 11 June 2014

I’m about to head off on a speak­ing tour to Europe (more on that in another post) and one of my hosts has asked for my pow­er­point slides so they can print them. They have made two false assump­tions: (1) that I use pow­er­point; (2) that my slides are sta­tic so they can be printed. Instead, I pro­duced a cut-​​​​down ver­sion of my beamer slides, leav­ing out some of the ani­ma­tions and other fea­tures that will not print eas­ily. Then I pro­duced a pdf file with sev­eral slides per page.

1 Comment  comments 

Interpreting noise

Published on 6 April 2014

When watch­ing the TV news, or read­ing news­pa­per com­men­tary, I am fre­quently amazed at the attempts peo­ple make to inter­pret ran­dom noise. For exam­ple, the lat­est tiny fluc­tu­a­tion in the share price of a major com­pany is attrib­uted to the CEO being ill. When the exchange rate goes up, the TV finance com­men­ta­tor con­fi­dently announces that it is a reac­tion to Chi­nese build­ing con­tracts. No one ever says “The unem­ploy­ment rate has dropped by 0.1% for no appar­ent rea­son.” What is going on here is that the com­men­ta­tors are assum­ing we live in a noise-​​​​free world. They imag­ine that every­thing is explic­a­ble, you just have to find the expla­na­tion. How­ever, the world is noisy — real data are sub­ject to ran­dom fluc­tu­a­tions, and are often also mea­sured inac­cu­rately. So to inter­pret every lit­tle fluc­tu­a­tion is silly and misleading.

4 Comments  comments 

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.

6 Comments  comments 

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.

No Comments  comments 

Interview for the Capital of Statistics

Published on 5 February 2014

Earo Wang recently inter­viewed me for the Chi­nese web­site Cap­i­tal of Sta­tis­tics. The Eng­lish tran­script of the inter­vew is on Earo’s per­sonal web­site. This is the third inter­view I’ve done in the last 18 months. The oth­ers were for: Data Min­ing Research. Repub­lished in Amstat News. DecisionStats.  

No Comments  comments 

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


2 Comments  comments 

Slides from my online forecasting course

Published on 23 January 2014

Last year I taught an online course on fore­cast­ing using R. The slides and exer­cise sheets are now avail­able at www​.otexts​.org/​f​p​p​/​r​e​s​o​u​rces/

7 Comments  comments 

Questions on my online forecasting course

Published on 4 October 2013

I’ve been get­ting emails ask­ing ques­tions about my upcom­ing course on Fore­cast­ing using R. Here are some answers.

7 Comments  comments