Blog Archives

Computational Actuarial Science with R

February 2, 2014
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Computational Actuarial Science with R

I recently co-authored a chapter on “Prospective Life Tables” for this book, edited by Arthur Charpentier. R code to reproduce the figures and to complete the exercises for our chapter is now available on github. Code for the other chapters should also be available soon. The book can be pre-ordered on Amazon.

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Automatic time series forecasting in Granada

January 30, 2014
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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...

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Free books on statistical learning

January 29, 2014
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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 machine...

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Time series data in R

January 28, 2014
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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...

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New in forecast 5.0

January 26, 2014
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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...

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Thoughts on the Ljung-Box test

January 23, 2014
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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    ...

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Slides from my online forecasting course

January 22, 2014
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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/

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Looking for a new post-doc

January 21, 2014
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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...

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Estimating a nonlinear time series model in R

January 20, 2014
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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:     where is a Gaussian white noise series with variance . The following function will generate...

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Judgmental forecasting experiment

December 22, 2013
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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 researchers to participate, and are offering £50 Amazon Gift Cards...

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