A very useful way of keeping up with blogs in a particular area is to subscribe to a blog aggregator. These will syndicate posts from a large number of blogs and provide links back to the original sources. So you only need to subscribe once to get all the good stuff in that area.
There are now several blog aggregators available that might be of interest to readers here. And this blog is now syndicated on several other sites including those listed below. (more…)
Forecasting competitions are a great way to test new methods and obtain a realistic evaluation of how good they are. So I’m delighted that the IEEE is organizing an energy forecasting competition as outlined by Tao Hong below. (more…)
Every day I receive emails, or comments on this blog, asking for help with R, forecasting, LaTeX, possible research topics, how to install software, or some other thing I’m supposed to know something about. Unfortunately, I cannot provide a one-man help service to the rest of the world. I used to reply to all the requests explaining where to go for help, but I stopped replying a while ago as it took too much time to do even that.
If you want help, please ask at either stats.stackexchange.com (for R or statistics questions) or tex.stackexchange.com (for LaTeX questions).
Unless you are one of my students, the only questions I will answer are ones that concern my R packages or research papers. And even then, I won’t reply if the answer is in the help files. I write those help files for a reason, so please read them.
I’m sorry I can’t do more, but if I did everything people ask me to do, I’d never write any papers or produce any R packages, and I think that’s a better use of my time.
A few years ago, I was working on a project where we measured various characteristics of a time series and used the information to determine what forecasting method to apply or how to cluster the time series into meaningful groups. The two main papers to come out of that project were:
I’ve since had a lot of requests for the code which one of my coauthors has been helpfully emailing to anyone who asked. But to make it easier, we thought it might be helpful if I post some updated code here. This is not the same as the R code we used in the paper, as I’ve improved it in several ways (so it will give different results). If you just want the code, skip to the bottom of the post. (more…)
Some of the most popular pages on this site are my LaTeX templates: for a curriculum vitae, a beamer poster, a beamer talk, a Monash University working paper and a Monash University thesis. Almost all new LaTeX users begin with templates, so it is surprising that there aren’t more good templates around to get people started.
Now there is a great new website for LaTeX templates: www.latextemplates.com. There are some nice templates for letters, lab reports, calendars, theses, assignments, essays, and CVs. The templates are well-structured with lots of comments to make it easy to understand how they work, and to make modifications. Even experienced LaTeXers will probably learn some new tricks and new packages from browsing the templates.
Google has produced another great tool for researchers, this time providing some metrics on journal citations. Google Scholar Metrics allows you to search on journals rather than articles, and to see the average or median h-index for each journal.
For example, a search on “forecasting” yields the following results:
The forecast package uses the base R graphics for all plots, but some people may prefer to use the nice graphics available using the ggplot2 package.
In the following two posts, Frank Davenport shows how it can be done:
XeLaTeX is a replacement for pdfLaTeX that allows you to use the fonts on your computer (rather than only those fonts that come with your tex system). However, TeXstudio is not set up to use XeLaTeX yet.
Fortunately, it is not difficult. Go to Options/Commands where all the commands used by TeXstudio are specified. You probably don’t need standard LaTeX these days, so replace the LaTeX command with the following.
xelatex -interaction=nonstopmode %.tex
Then click OK.
Now the LaTeX button at the top of the screen is mapped to XeLaTeX rather than standard LaTeX. You can still access pdfLaTeX via its button for your non-XeLaTeX files.
Before anyone comments that you need standard LaTeX for when eps graphics are used, see Converting eps to pdf.
For those who have not read the seminal works of Tufte and Cleveland, please hang your heads in shame. To salvage some sense of self-worth, you can then head over to Solomon Messing’s blog where he is starting a series on data visualization based on the principles developed by Tufte and Cleveland (with R examples).
The classics are also worth reading, and remain relevant despite the 20 or 30 years that have elapsed since they appeared.
I have thought quite a lot about including regressors (i.e. covariates) in exponential smoothing (ETS) models, and I have done it a couple of times in my published work. See my 2008 exponential smoothing book (chapter 9) and my 2008 Tourism Management paper. However, there are some theoretical issues with these approaches, which have come to light through the research of Ahmad Farid Osman, one of our PhD students at Monash University. Basically, they are never forecastable in the sense explained in Section 10.2 my 2008 book (forecastability is the ETS equivalent of invertibility in ARIMA models).
Osman has attempted to repair the problem by proposing a different formulation from those in the above references. The only public description of his proposed model is given by Osman and King in this presentation – sorry, they do have a full paper explaining their approach, but it is not publicly available. However, the model is much messier than the formulation we put in our book, and although it avoids the forecastability issues, I think it is more difficult to interpret. Still, it’s a good attempt at a tough problem, and there’s nothing else around that’s any better.
So don’t expect any code for fitting ETS models with regressors to appear in the forecast package for R anytime soon, and maybe never. Osman may at some stage make his own code available.
Right now, if I have a forecasting problem where I want to use covariates, I tend to use regression with ARMA errors. That’s easy to do using the Arima() or auto.arima() functions in the forecast package for R. It is even possible to handle multiple seasonality in that way with Fourier terms (although that forces the seasonality to be unchanging over time). More flexible models are possible using the arimax() function in the TSA package.
Of course, there is always the dynamic linear model approach, implemented in the dynlm package.
Rob J Hyndman is Professor of Statistics at Monash University, Australia.
Email: Rob.Hyndman@monash.edu
