Blog Archives

Forecasting with R

September 25, 2013
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The following video has been produced to advertise my upcoming course on Forecasting with R, run in partnership with Revolution Analytics. The course will run from 21 October to 4 December, for two hours each week. More details are available at http:/...

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Forecasting with daily data

September 16, 2013
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Forecasting with daily data

I’ve had several emails recently asking how to forecast daily data in R. Unless the time series is very long, the simplest approach is to simply set the frequency attribute to 7. y <- ts(x, frequency=7) Then any of the usual time series forecasting methods should produce reasonable forecasts. For example library(forecast) fit <- ets(y) fc <- forecast(fit) plot(fc)...

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Online course on forecasting using R

September 10, 2013
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I am teaming up with Revolution Analytics to teach an online course on forecasting with R. Topics to be covered include seasonality and trends, exponential smoothing, ARIMA modelling, dynamic regression and state space models, as well as forecast accuracy methods and forecast evaluation techniques such as cross-validation. I will talk about some of my consulting experiences, and explain the...

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Reflections on UseR! 2013

July 12, 2013
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This week I’ve been at the R Users conference in Albacete, Spain. These conferences are a little unusual in that they are not really about research, unlike most conferences I attend. They provide a place for people to discuss and exchange ideas on how R can be used. Here are some thoughts and highlights of the conference, in no...

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Facts and fallacies of the AIC

July 3, 2013
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Akaike’s Information Criterion (AIC) is a very useful model selection tool, but it is not as well understood as it should be. I frequently read papers, or hear talks, which demonstrate misunderstandings or misuse of this important tool. The following points should clarify some aspects of the AIC, and hopefully reduce its misuse. The AIC is a penalized likelihood,...

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Forecasting annual totals from monthly data

May 15, 2013
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Forecasting annual totals from monthly data

This question was posed on crossvalidated.com: I have a monthly time series (for 2009–2012 non-stationary, with seasonality). I can use ARIMA (or ETS) to obtain point and interval forecasts for each month of 2013, but I am interested in forecasting the total for the whole year, including prediction intervals. Is there an easy way in R to obtain interval...

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My new forecasting book is finally finished

April 20, 2013
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My new online forecasting book (written with George Athanasopoulos) is now completed. I previously described it on this blog nearly a year ago. In reality, an online book is never complete, and we plan to continually update it. But it is now at the point where it is suitable for course work, and contains exercises and references. We hope...

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ETS models now in EViews 8

February 28, 2013
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ETS models now in EViews 8

The ETS modelling framework developed in my 2002 IJF paper (with Koehler, Snyder and Grose), and in my 2008 Springer book (with Koehler, Ord and Snyder), is now available in EViews 8. I had no idea they were even working on it, so it was quite a surprise to be told that EViews now includes ETS models. Here is the blurb...

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Removing white space around R figures

February 21, 2013
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When I want to insert figures generated in R into a LaTeX document, it looks better if I first remove the white space around the figure. Unfortunately, R does not make this easy as the graphs are generated to look good on a screen, not in a document. There are two things that can be done to fix this...

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Out-of-sample one-step forecasts

February 13, 2013
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It is common to fit a model using training data, and then to evaluate its performance on a test data set. When the data are time series, it is useful to compute one-step forecasts on the test data. For some reason, this is much more commonly done by people trained in machine learning rather than statistics. If you are...

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