Publications by Rob J Hyndman
Three jobs at Monash
We are currently advertising for three academic positions, suitable for recent PhD graduates. Lecturer (Applied Statistics or Operations Research) Five-year position with MAXIMA and the School of Mathematical Sciences Two positions available. Applications close 31 October. More information. Lecturer (Econometrics/Business Statistics) Continuin...
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Judgmental forecasting experiment
The Centre for Forecasting at Lancaster University is conducting some research on judgmental forecasting and model selection. They hope to compare the performance of judgmental model selection with statistical model selection, in order to learn how to best design forecasting support systems. They would like forecasting students, practitioners and...
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Estimating a nonlinear time series model in R
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: r$} \end{cases}\]” title=”Rendered by QuickLaTeX.com”/> where is a Gaussian white noise series with ...
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Looking for a new post-doc
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 ...
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Slides from my online forecasting course
Last year I taught an online course on forecasting using R. The slides and exercise sheets are now available at www.otexts.org/fpp/resources/ Related ...
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Thoughts on the Ljung-Box test
It is common to use a Ljung-Box test to check that the residuals from a time series model resemble white noise. However, there is very little practical advice around about how to choose the number of lags for the test. The Ljung-Box test was proposed by Ljung and Box (Biometrika, 1978) and is based on the statistic where is the length of the ti...
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New in forecast 5.0
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. Handling missing values and outliers Data cleaning is often the first step that data scientists and analysts t...
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Time series data in R
There is no shortage of time series data available on the web for use in student projects, or self-learning, or to test out new forecasting algorithms. It is now relatively easy to access these data sets directly in R. M Competition data The 1001 series from the M-competition and the 3003 series from the M3-competition are available as part of th...
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Free books on statistical learning
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 mac...
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Automatic time series forecasting in Granada
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 pr...
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