I received this email yesterday: I have been using your ‘forecast’ package for more than a year now. I was on R version 2.15 until last week, but I am having issues with lubridate package, hence decided to update R version to R 3.0.1. In our organization even getting an open source application require us to go through a whole lot of approval processes. I asked for R 3.0.1, before I get approval for 3.0.1, a new version of R ( R 3.0.2 ) came out. Unfortunately for me forecast package was built in R3.0.2. Is there any version of forecast package that works in older version of R(3.0.1). I just don’t want to go through this entire approval war again within the organization. Please help if you have any work around for this This is unfortunately very common. Many corporate IT environments lock down computers to such an extent that it cripples the use of modern software like R which is continuously updated. It also affects universities (which should know better) and I am constantly trying to invent work-arounds to the constraints that Monash IT services place on staff and student computers. Here are a few thoughts that might help.
Posts Tagged ‘computing’:
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.
Earlier this week I had coffee with Ben Fulcher who told me about his online collection comprising about 30,000 time series, mostly medical series such as ECG measurements, meteorological series, birdsong, etc. There are some finance series, but not many other data from a business or economic context, although he does include my Time Series Data Library. In addition, he provides Matlab code to compute a large number of characteristics. Anyone wanting to test time series algorithms on a large collection of data should take a look. Unfortunately there is no R code, and no R interface for downloading the data.
A new version of my hts package for R is now on CRAN. It was completely re-written from scratch. Not a single line of code survived. There are some minor syntax changes, but the biggest change is speed and scope. This version is many times faster than the previous version and can handle hundreds of thousands of time series without complaining.
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.
Last week, version 5.0 of the forecast package for R was released. There are a few new functions and changes made to the package, which is why I increased the version number to 5.0. Thanks to Earo Wang for helping with this new version.
We are looking for a new post-doctoral research fellow to work on the project “Macroeconomic Forecasting in a Big Data World”. Details are given at the link below jobs.monash.edu.au/jobDetails.asp?sJobIDs=519824 This is a two year position, funded by the Australian Research Council, and working with me, George Athanasopoulos, Farshid Vahid and Anastasios Panagiotelis. We are looking for someone with a PhD in econometrics, statistics or machine learning, who is well-trained in computationally intensive methods, and who has a background in at least one of time series analysis, macroeconomic modelling, or Bayesian econometrics.
There are quite a few R packages available for nonlinear time series analysis, but sometimes you need to code your own models. Here is a simple example to show how it can be done. The model is a first order threshold autoregression: where is a Gaussian white noise series with variance . The following function will generate some random data from this model.
I’ve been getting emails asking questions about my upcoming course on Forecasting using R. Here are some answers.
The following video has been produced to advertise my upcoming course on Forecasting with R, run in partnership with Revolution Analytics.