Blog Archives

Estimating a nonlinear time series model in R

January 20, 2014
By
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...

Read more »

Judgmental forecasting experiment

December 22, 2013
By

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...

Read more »

Three jobs at Monash

October 17, 2013
By

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) Continuing position with the Department of Econometrics and Business Statistics Applications close 31 January 2014. More information. Please...

Read more »

Questions on my online forecasting course

October 3, 2013
By

I’ve been getting emails asking questions about my upcoming course on Forecasting using R. Here are some answers. Do I need to use the Revolution Enterprise version of R, or can I use open-source R? Open source R is fine. Revolution Analytics is organizing the course, but there is no requirement to use their software. I will be using...

Read more »

Forecasting with R

September 25, 2013
By

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:/...

Read more »

Forecasting with daily data

September 16, 2013
By
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)...

Read more »

Online course on forecasting using R

September 10, 2013
By

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...

Read more »

Reflections on UseR! 2013

July 12, 2013
By

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...

Read more »

Facts and fallacies of the AIC

July 3, 2013
By

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,...

Read more »

Forecasting annual totals from monthly data

May 15, 2013
By
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...

Read more »