Blog Archives

FPP now available as a downloadable e-book

September 20, 2014
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FPP now available as a downloadable e-book

My forecasting textbook with George Athanasopoulos is already available online (for free), and in print via Amazon (for under $40). Now we have made it available as a downloadable e-book via Google Books (for $15.55). The Google Books version is identical to the print version on Amazon (apart from a few typos that have been fixed). To use

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Generating quantile forecasts in R

September 7, 2014
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Generating quantile forecasts in R

From today’s email: I have just finished reading a copy of ‘Forecasting:Principles and Practice’ and I have found the book really interesting. I have particularly enjoyed the case studies and focus on practical applications. After finishing the book I have joined a forecasting competition to put what I’ve learnt to the test. I do have

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Resources for the FPP book

September 2, 2014
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The FPP resources page has recently been updated with several new additions including R code for all examples in the book. This was already available within each chapter, but the examples have been collected into one file per chapter to save copying and pasting the various code fragments. Slides from a course on Predictive Analytics from the University of Sydney....

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A new candidate for worst figure

August 31, 2014
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A new candidate for worst figure

Today I read a paper that had been submitted to the IJF which included the following figure along with several similar plots. (Click for a larger version.) I haven’t seen anything this bad for a long time. In fact, I think I would find it very difficult to reproduce using R, or even Excel (which is particularly adept at...

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Forecasting with R in WA

August 24, 2014
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On 23–25 September, I will be running a 3-day workshop in Perth on “Forecasting: principles and practice” mostly based on my book of the same name. Workshop participants will be assumed to be familiar with basic statistical tools such as multiple regression, but no knowledge of time series or forecasting will be assumed. Some prior experience in R is...

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GEFCom 2014 energy forecasting competition is underway

August 17, 2014
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GEFCom 2014 is the most advanced energy forecasting competition ever organized, both in terms of the data involved, and in terms of the way the forecasts will be evaluated. So everyone interested in energy forecasting should head over to the competition webpage and start forecasting: www.gefcom.org. This time, the competition is hosted on CrowdANALYTIX rather than Kaggle. Highlights of GEFCom2014: An...

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Visit of Di Cook

August 12, 2014
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Visit of Di Cook

Next week, Professor Di Cook from Iowa State University is visiting my research group at Monash University. Di is a world leader in data visualization, and is especially well-known for her work on interactive graphics and the XGobi and GGobi software. See her book with Deb Swayne for details. For those wanting to hear her speak, read on. Research seminar She...

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Minimal reproducible examples

August 10, 2014
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I occasionally get emails from people thinking they have found a bug in one of my R packages, and I usually have to reply asking them to provide a minimal reproducible example (MRE). This post is to provide instructions on how to create a MRE. Bug reports on github, not email First, if you think there is a bug,...

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Coherent population forecasting using R

July 23, 2014
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Coherent population forecasting using R

This is an example of how to use the demography package in R for stochastic population forecasting with coherent components. It is based on the papers by Hyndman and Booth (IJF 2008) and Hyndman, Booth and Yasmeen (Demography 2013). I will use Australian data from 1950 to 2009 and forecast the next 50 years. In demography, “coherent” forecasts are...

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Plotting the characteristic roots for ARIMA models

July 23, 2014
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Plotting the characteristic roots for ARIMA models

When modelling data with ARIMA models, it is sometimes useful to plot the inverse characteristic roots. The following functions will compute and plot the inverse roots for any fitted ARIMA model (including seasonal models). # Compute AR roots arroots <- function(object) { if(class(object) != "Arima" & class(object) != "ar") stop("object must be of class Arima or ar") if(class(object) ==...

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