Interview with Rob J Hyndman

Rob J. Hyndman is Professor of Statistics at Monash University, Australia and Editor-in-Chief of the International Journal of Forecasting. He is also the author of several widely used R packages such as forecast and hts on time series forecasts.

Earo: Your bachelor degree is a science honours degree. So why did you choose Statistics as your major and what did statistics interest you most at that moment?

Rob: When I first did my science degree, I wasn’t thinking of statistics and I was planning to do mathematics. At that time, majoring in any mathematical discipline at Melbourne University required first-year students to do Statistics, Mathematics and Computer Science. So I ended up doing Statistics since I had to. But I found it interesting, because I love the idea that I can solve practical problems using mathematical tools.

At the end of my first-year, I got a summer vacation debugging code for the Statistical Consulting Centre at Melbourne University. After I had found the bugs, I tried to get a time to talk to my boss about what I had done, but he was always busy. I knew he had to write a report for the client, so I eventually just wrote the report myself and left in on my boss’s desk. Later he called to me to say “I think we should employ you permanently”. By the end of that summer, I decided to major in Statistics. The following year, when I was a second-year student, I spoke at an academic conference on one of my consulting projects, because the client wanted me to talk about one of the jobs that I had done. I was pretty involved in the consulting work there and I did enjoy it. That’s why I became a Statistician.

Earo: Since you’ve been an academic for more than two decades, has the current structure of Statistics remained the same as that of those years when you were an undergraduate?

Rob: The undergraduate courses are quite similar. Maybe it should change more. (But how?) For a start, I think more computing should be put into statistics training, especially with data sets becoming much larger and more complicated. It will change, of course, but universities are very slow to update at the undergraduate level, especially the first couple of years. On the other hand, higher level courses are updated all the time in any good university.

Earo: You’ve mentioned we have to accept more computing training due to the big data era. So how do you define the big data and how do you think the relationship between data science and statistics?

Rob: I’ll grab the definition from someone else: If the problems take longer to compute than to set up the model mathematically, then it is a big data problem. The hard work of most statistical analysis is to define the model rather than to do the computation, since the computer is quick. But if it is the other way around, it’s definitely a large dataset. The other definition of the big data is when the data cannot fit into the memory of your computer. How does data science relate to statistics? I think statistics is a subset of data science. Data science is a bigger topic including data management and data storage etc.; whereas statistics is mostly about the randomisation of the dataset and how to analyse it and how to report. But the actual management of large datasets is an important area of data science, but is often ignored in statistics. Many people think that data science is a sexy word for statistics. But I tend to think it is a bit more than that.

Earo: Because of the feature of data science, there are so many computer scientists getting involved in the field of data science. What advantages does a Statistician have over a Computer Scientist?

Rob: The big advantage is understanding stochastic modelling. Most computer scientists don’t have any real training in stochastic models, and they often don’t understand some concepts like uncertainty and how you handle noisy data. In data science problems, a common task is to extract information out of noisy data, and statisticians have a big advantage there. Another issue is quantifying uncertainty. Typically, in prediction problems, Data Scientists don’t produce prediction intervals and just produce the point forecasts. But there’s uncertainty associated with the forecasts, and you can quantify if you have stochastic components in the process. That’s a whole way of thinking which has been in Statistics for more than a hundred years. Many Computer Scientists getting into data science don’t know that way of thinking about uncertainty. On the other hand, they are good at other aspects, such as algorithm design and efficient computation. I think Computer Scientists and Statisticians can work very well together because they bring slightly different perspectives to a problem. Ultimately, I expect the distinction between data scientist and statistician will disappear, but that’s a few years away.

Earo: The forecast package has been one of the most widely used ones at R CRAN. So can you please talk about the reason why you developed forecast package?

Rob: The first reason is I had some sets of functions that I used for consulting. I thought it’s neat to have those in a package and a lot of people don’t have access to good tools or they don’t know how to find good tools. I figured that I could make my forecast functions available for other people and that would be useful. The other reason was that I have developed a lot of new forecasting methods. I want people to use them, and the only way that people are going to use them is to give them software. Thirty years ago, if you wrote a paper on a new method or model and someone was interested in applying it, they had to code it up themselves. There was no culture of sharing code. But that changed. If you are serious about developing new statistical models or methods, then you have to provide R packages to make it easy for other people to implement your ideas.

Earo: You not only develop R packages that are available on the web, you are also a blogger and an active member at Cross Validated . Now you are a co-founder of OTexts which offers online and open-access textbooks. From my point of view, you’re quite involved in the online statistical community. What’s your motivation to contribute your hard work and time for free?

Rob: I figure I get paid by the university (and indirectly by the Australian government) to develop new ideas in statistics. It makes sense for me to give my work back to the community. I can’t see the point in locking it up in journals. If you want to make a difference, you have to make it available. It’s much better way to do statistical research in the open and encourage open conversation. I think the discipline will advance faster and the information will be more widely available that way. My salary is paid so I don’t have to think about making money out of the work I do. I don’t really understand why some academics will not work openly. It’s different if you are working in private industry and having to make a living through your research; but I don’t have to do that.

Why do I blog? Because I want to influence the way people think and I figure writing things down on a blog actually helps do that. I also blog because I find myself answering the same questions from my students and others. I hate doing the same thing over again, so I just stick my answers on my website and people can read them there. If I see repeated questions, I’ll write a post. At least, it helps me answer questions more efficiently. I also blog because it’s a good advertisement for my own research and I want my research to be used. For example, I might write a blog post discussing the problem of multiple seasonality in forecasting, and explaining that the problem is handled by a method implemented in my forecast package. Nobody in business will read the academic paper where I develop the theory, but they might do a web search and find an example on my website and use it. And then my work gets used more and that’s a good thing. I don’t want to write a paper that gets published but never gets used. So there are a few reasons why I do the blogging.

Cross Validated also began because I receive lots of questions about statistics generally and I thought that it would be nice if there was a website where such questions could be answered. A lot of people are doing statistics with insufficient training to really understand what they’re doing. If they could get some support from an online community that would be good. I was also aware of the stackoverflow community for programmers where you can ask programming questions and get amazingly good answers really fast. I use it occasionally myself when I have programming questions, usually about R but sometimes about regular expressions that I am trying to use. So I thought it would be really cool if there was a statistical version of this site. I proposed the idea to the stackoverflow company, and I became the first administrator of that site, and managed to get things working well for six months. I’m still on it but I’m no longer an administrator.

The textbook is different. It really goes back a long way. I wrote a textbook on forecasting in 1998 and it became the biggest selling textbook on statistical forecasting in the world. My contract said I should get 5% of sales, but I actually got much less. Wiley (the publisher) had no costs apart from printing and marketing, and they did a lousy job in marketing. So I really wasn’t happy. And then the book became out-of-date. I thought I needed to do another edition but I didn’t want to do another edition with Wiley. However my contract said they had the rights for the next edition and I wasn’t allowed to write a competitive book. After negotiating backwards and forwards a few times, they eventually agreed to release my from that contract.

I was thinking that the reason I write a book is not to make money; the real reason that I write a book is because I want to influence the way people think and my view of forecasting hopefully influences other people to do forecasting well. You write a textbook because you want to change the world; not because you want to make money. It appears that most students don’t want to spend money on textbooks since they are often only used for one semester. Either the students get hold of a second-hand copy, which is often out-of-date, or they just look on the web to try to find some other resource instead of using a book. There are few good-quality textbooks on the web, and what is there is often incorrect or incomplete. So I thought I would write an online book. I didn’t care about making money, everyone would have access to it, and I wouldn’t have to worry about whether people could afford it. So I wrote an online book and it quickly became very widely used. As I put chapters online, they started receiving a lot of traffic. People like online things as long as they are good quality by reputable people. So then I set up the OTexts platform to see if we can encourage other academics to write online books. We do have some plans to make money as well. But we haven’t yet started monetizing it. The books will always be free as that is an important underlying philosophy of OTexts.

Earo: You are a statistical consultant in public or private sectors. How does the consulting work relate to your research and teaching?

Rob: My research and consulting are quite closely related and they feed on each other. A company will come to me with a problem that they can’t solve. If the problem has an existing solution, I generally won’t do it. I’m not interested in doing routine consulting work applying existing solutions. But if it sounds hard and there’s no obvious way to do it using existing methods, I’ll usually take it on, because it’s an interesting problem and it might lead to publishable research. The ideal consulting job will lead to papers. Even if it isn’t directly to papers, consulting helps me to stay aware of what are the real problems in business and in industry and that guides what research problems I take on.

To give you an example, years ago a company asked me to help solve a forecasting problem that involved the monthly sales of thousands of different products. They wanted to forecast the individual sales of each product, as well as the grouped sales by retail outlet or by product type. I helped them to solve the problem but all the time I was thinking there is a theoretical problem here about how to forecast hierarchical time series in an efficient way. About ten years after I first encountered this problem in consulting, I wrote my first paper on hierarchical time series modeling. And I am still working on hierarchical forecasting issues and methods. I find that I’m using the consulting problems to motivate what I might do research on.

It also works the other way as well. A company will come along with a problem that I have already done some research on. Then I might take on the project so I can see how well my research works in practice. In particular, I want to know where my methods do not work, because nothing works perfectly. That might guide me to other research problems. I don’t really see my consulting as separate from my research, because I try to take on consulting problems that are research-like.

Occasionally, I’ll do free consulting work that is good for the country but with no real research component. For twenty years, I’ve been involved with the scaling of all year 12 marks in Victoria. The algorithm takes students’ marks and scales them in a way that enables them to be comparable across all subjects. These rescaled scores are then used for selecting students to go on to university. I do this work unpaid because I think it’s important. I’m also an advisor to the Australian Bureau of Statistics. I advise them on methodology they should use in collecting and analysing data for the government.

My consulting is also useful in teaching, because it gives me stories to tell and students usually like to hear about things I’ve done. It’s nice when everything works together – research, consulting and teaching.