Interview Rob J Hyndman Forecasting Expert #rstats

Here is an interview with Prof Rob J Hyndman who has created many time series forecasting methods and authored books as well as R packages on the same.

Ajay -Describe your journey from being a student of science to a Professor. What were some key turning points along that journey?
Rob- I started a science honours degree at the University of Melbourne in 1985. By the end of 1985 I found myself simultaneously working as a statistical consultant (having completed all of one year of statistics courses!). For the next three years I studied mathematics, statistics and computer science at university, and tried to learn whatever I needed to in order to help my growing group of clients. Often we would cover things in classes that I’d already taught myself through my consulting work. That really set the trend for the rest of my career. I’ve always been an academic on the one hand, and a statistical consultant on the other. The consulting work has led me to learn a lot of things that I would not otherwise have come across, and has also encouraged me to focus on research problems that are of direct relevance to the clients I work with.
I never set out to be an academic. In fact, I thought that I would get a job in the business world as soon as I finished my degree. But once I completed the degree, I was offered a position as a statistical consultant within the University of Melbourne, helping researchers in various disciplines and doing some commercial work. After a year, I was getting bored doing only consulting, and I thought it would be interesting to do a PhD. I was lucky enough to be offered a generous scholarship which meant I was paid more to study than to continue working.
Again, I thought that I would probably go and get a job in the business world after I finished my PhD. But I finished it early and my scholarship was going to be cut off once I submitted my thesis. So instead, I offered to teach classes for free at the university and delayed submitting my thesis until the scholarship period ran out. That turned out to be a smart move because the university saw that I was a good teacher, and offered me a lecturing position starting immediately I submitted my thesis. So I sort of fell into an academic career.
I’ve kept up the consulting work part-time because it is interesting, and it gives me a little extra money. But I’ve also stayed an academic because I love the freedom to be able to work on anything that takes my fancy.
Ajay- Describe your upcoming book on Forecasting.
Rob- My first textbook on forecasting (with Makridakis and Wheelwright) was written a few years after I finished my PhD. It has been very popular, but it costs a lot of money (about $140 on Amazon). I estimate that I get about $1 for every book sold. The rest goes to the publisher (Wiley) and all they do is print, market and distribute it. I even typeset the whole thing myself and they print directly from the files I provided. It is now about 15 years since the book was written and it badly needs updating. I had a choice of writing a new edition with Wiley or doing something completely new. I decided to do a new one, largely because I didn’t want a publisher to make a lot of money out of students using my hard work.
It seems to me that students try to avoid buying textbooks and will search around looking for suitable online material instead. Often the online material is of very low quality and contains many errors.
As I wasn’t making much money on my textbook, and the facilities now exist to make online publishing very easy, I decided to try a publishing experiment. So my new textbook will be online and completely free. So far it is about 2/3 completed and is available at I am hoping that my co-author (George Athanasopoulos) and I will finish it off before the end of 2012.
The book is intended to provide a comprehensive introduction to forecasting methods. We don’t attempt to discuss the theory much, but provide enough information for people to use the methods in practice. It is tied to the forecast package in R, and we provide code to show how to use the various forecasting methods.
The idea of online textbooks makes a lot of sense. They are continuously updated so if we find a mistake we fix it immediately. Also, we can add new sections, or update parts of the book, as required rather than waiting for a new edition to come out. We can also add richer content including video, dynamic graphics, etc.
For readers that want a print edition, we will be aiming to produce a print version of the book every year (available via Amazon).
I like the idea so much I’m trying to set up a new publishing platform ( to enable other authors to do the same sort of thing. It is taking longer than I would like to make that happen, but probably next year we should have something ready for other authors to use.
Ajay- How can we make textbooks cheaper for students as well as compensate authors fairly
Rob- Well free is definitely cheaper, and there are a few businesses trying to make free online textbooks a reality. Apart from my own efforts, is producing a lot of free textbooks. And is another great resource.
With, we will compensate authors in two ways. First, the print versions of a book will be sold (although at a vastly cheaper rate than other commercial publishers). The royalties on print sales will be split 50/50 with the authors. Second, we plan to have some features of each book available for subscription only (e.g., solutions to exercises, some multimedia content, etc.). Again, the subscription fees will be split 50/50 with the authors.
Ajay- Suppose a person who used to use forecasting software from another company decides to switch to R. How easy and lucid do you think the current documentation on R website for business analytics practitioners such as these – in the corporate world.
Rob- The documentation on the R website is not very good for newcomers, but there are a lot of other R resources now available. One of the best introductions is Matloff’s “The Art of R Programming”. Provided someone has done some programming before (e.g., VBA, python or java), learning R is a breeze. The people who have trouble are those who have only ever used menu interfaces such as Excel. Then they are not only learning R, but learning to think about computing in a different way from what they are used to, and that can be tricky. However, it is well worth it. Once you know how to code, you can do so much more.  I wish some basic programming was part of every business and statistics degree.
If you are working in a particular area, then it is often best to find a book that uses R in that discipline. For example, if you want to do forecasting, you can use my book ( Or if you are using R for data visualization, get hold of Hadley Wickham’s ggplot2 book.
Ajay- In a long and storied career- What is the best forecast you ever made ? and the worst?
 Rob- Actually, my best work is not so much in making forecasts as in developing new forecasting methodology. I’m very proud of my forecasting models for electricity demand which are now used for all long-term planning of electricity capacity in Australia (see  for the details). Also, my methods for population forecasting ( ) are pretty good (in my opinion!). These methods are now used by some national governments (but not Australia!) for their official population forecasts.
Of course, I’ve made some bad forecasts, but usually when I’ve tried to do more than is reasonable given the available data. One of my earliest consulting jobs involved forecasting the sales for a large car manufacturer. They wanted forecasts for the next fifteen years using less than ten years of historical data. I should have refused as it is unreasonable to forecast that far ahead using so little data. But I was young and naive and wanted the work. So I did the forecasts, and they were clearly outside the company’s (reasonable) expectations, and they then refused to pay me. Lesson learned. It’s better to refuse work than do it poorly.

Probably the biggest impact I’ve had is in helping the Australian government forecast the national health budget. In 2001 and 2002, they had underestimated health expenditure by nearly $1 billion in each year which is a lot of money to have to find, even for a national government. I was invited to assist them in developing a new forecasting method, which I did. The new method has forecast errors of the order of plus or minus $50 million which is much more manageable. The method I developed for them was the basis of the ETS models discussed in my 2008 book on exponential smoothing (

. And now anyone can use the method with the ets() function in the forecast package for R.
Rob J Hyndman is Pro­fessor of Stat­ist­ics in the Depart­ment of Eco­no­met­rics and Busi­ness Stat­ist­ics at Mon­ash Uni­ver­sity and Dir­ector of the Mon­ash Uni­ver­sity Busi­ness & Eco­nomic Fore­cast­ing Unit. He is also Editor-in-Chief of the Inter­na­tional Journal of Fore­cast­ing and a Dir­ector of the Inter­na­tional Insti­tute of Fore­casters. Rob is the author of over 100 research papers in stat­ist­ical sci­ence. In 2007, he received the Moran medal from the Aus­tralian Academy of Sci­ence for his con­tri­bu­tions to stat­ist­ical research, espe­cially in the area of stat­ist­ical fore­cast­ing. For 25 years, Rob has main­tained an act­ive con­sult­ing prac­tice, assist­ing hun­dreds of com­pan­ies and organ­iz­a­tions. His recent con­sult­ing work has involved fore­cast­ing elec­tri­city demand, tour­ism demand, the Aus­tralian gov­ern­ment health budget and case volume at a US call centre.

The economics of software piracy

Software piracy exists because-

1) Lack of appropriate technological controls (like those on DVDs) or on Bit Torrents (an innovation on the centralized server like Napster) or on Streaming etc etc.

Technology to share content has evolved at a much higher pace than technology to restrict content from being shared or limited to purchasers.

2) Huge difference in purchasing power across the globe.

An Itunes song at 99 cents might be okay buy in USA, but in Asia it is very expensive. Maybe if content creators use Purchasing Power Parity to price their goods, it might make an indent.

3) State sponsored intellectual theft as another form of economic warfare- this has been going on since the West stole gunpowder and silk from the Chinese, and Intel decided to win back the IP rights to the microprocessor (from the Japanese client)

4) Lack of consensus in policy makers across the globe on who gets hurt from IP theft, but complete consensus across young people in the globe that they are doing the right thing by downloading stuff for free.

5) There is no such thing as a free lunch. Sometimes software (and movie and songs) piracy help create demand across ignored markets – I always think the NFL can be huge in India if they market it.Sometimes it forces artists to commit suicide because they give up on the life of starving musician.

Mostly piracy has helped break profits of intermediaries between the actual creator and actual consumer.

So how to solve software piracy , assuming it is something that can be solved-

I dont know, but I do care.

I give most of my writings as CC-by-SA and that includes my poems. People (friends and family) sometimes pay me not to sing.

Pirates have existed and will exist as long as civilized men romanticize the notion of piracy and bicker between themselves for narrow gains.

  1. Ephesians 4:28 Let the thief no longer steal, but rather let him labor, doing honest work with his own hands, so that he may have something to share with anyone in need.
  2. A clean confession, combined with a promise never to commit the sin again, when offered before one who has the right to receive it, is the purest type of repentance.-Gandhi
  3. If you steal, I will wash your mouth with soap- Anonymous Mother.
  4. You shall not steal- Moses
  5. Steal may refer to: Theft, the illegal taking of another person’s property without that person’s freely-given consent; The gaining of a stolen base in baseball;



Interview Prof Benjamin Alamar , Sports Analytics

Here is an interview with Prof Benjamin Alamar, founding editor of the Journal of Quantitative Analysis in Sport, a professor of sports management at Menlo College and the Director of Basketball Analytics and Research for the Oklahoma City Thunder of the NBA.

Ajay – The movie Moneyball recently sparked out mainstream interest in analytics in sports.Describe the role of analytics in sports management

Benjamin- Analytics is impacting sports organizations on both the sport and business side.
On the Sport side, teams are using analytics, including advanced data management, predictive anlaytics, and information systems to gain a competitive edge. The use of analytics results in more accurate player valuations and projections, as well as determining effective strategies against specific opponents.
On the business side, teams are using the tools of analytics to increase revenue in a variety of ways including dynamic ticket pricing and optimizing of the placement of concession stands.
Ajay-  What are the ways analytics is used in specific sports that you have been part of?

Benjamin- A very typical first step for a team is to utilize the tools of predictive analytics to help inform their draft decisions.

Ajay- What are some of the tools, techniques and software that analytics in sports uses?
Benjamin- The tools of sports analytics do not differ much from the tools of business analytics. Regression analysis is fairly common as are other forms of data mining. In terms of software, R is a popular tool as is Excel and many of the other standard analysis tools.
Ajay- Describe your career journey and how you became involved in sports management. What are some of the tips you want to tell young students who wish to enter this field?

Benjamin- I got involved in sports through a company called Protrade Sports. Protrade initially was a fantasy sports company that was looking to develop a fantasy game based on advanced sports statistics and utilize a stock market concept instead of traditional drafting. I was hired due to my background in economics to develop the market aspect of the game.

There I met Roland Beech (who now works for the Mavericks) and Aaron Schatz (owner of and learned about the developing field of sports statistics. I then changed my research focus from economics to sports statistics and founded the Journal of Quantitative Analysis in Sports. Through the journal and my published research, I was able to establish a reputation of doing quality, useable work.

For students, I recommend developing very strong data management skills (sql and the like) and thinking carefully about what sort of questions a general manager or coach would care about. Being able to demonstrate analytic skills around actionable research will generally attract the attention of pro teams.


Benjamin Alamar, Professor of Sport Management, Menlo College

Benjamin Alamar

Professor Benjamin Alamar is the founding editor of the Journal of Quantitative Analysis in Sport, a professor of sports management at Menlo College and the Director of Basketball Analytics and Research for the Oklahoma City Thunder of the NBA. He has published academic research in football, basketball and baseball, has presented at numerous conferences on sports analytics. He is also a co-creator of ESPN’s Total Quarterback Rating and a regular contributor to the Wall Street Journal. He has consulted for teams in the NBA and NFL, provided statistical analysis for author Michael Lewis for his recent book The Blind Side, and worked with numerous startup companies in the field of sports analytics. Professor Alamar is also an award winning economist who has worked academically and professionally in intellectual property valuation, public finance and public health. He received his PhD in economics from the University of California at Santa Barbara in 2001.

Prof Alamar is a speaker at Predictive Analytics World, San Fransisco and is doing a workshop there


All level tracks Track 1: Sports Analytics
Case Study: NFL, MLB, & NBA
Competing & Winning with Sports Analytics

The field of sports analytics ties together the tools of data management, predictive modeling and information systems to provide sports organization a competitive advantage. The field is rapidly developing based on new and expanded data sources, greater recognition of the value, and past success of a variety of sports organizations. Teams in the NFL, MLB, NBA, as well as other organizations have found a competitive edge with the application of sports analytics. The future of sports analytics can be seen through drawing on these past successes and the developments of new tools.

You can know more about Prof Alamar at his blog or journal at His detailed background can be seen at