I’ve been interviewed twice in the last year: For DecisionStats, 9 August 2012. For Data Mining Research, 21 October 2011. Republished in Amstat News, 1 December 2011. Some readers of this blog might find them interesting. I said a few things in t...
Posts Tagged ‘ forecasting ’
Forecasting the Olympics
Forecasting sporting events is a growing research area. The International Journal of Forecasting even had a special issue on sports forecasting a couple of years ago. The London 2012 Olympics has attracted a few forecasters trying to predict medal counts, world records, etc. Here are some of the articles I’ve seen. Which Olympic records get shattered?, Nate Silver, New...
Holt-Winters forecast using ggplot2
R has great support for Holt-Winter filtering and forecasting. I sometimes use this functionality, HoltWinter & predict.HoltWinter, to forecast demand figures based on historical data. Using the HoltWinter functions in R is pretty straightforward. Let's say our dataset looks as follows; demand <- ts(BJsales, start = c(2000, 1), frequency = Read more...
Time Series Data Library now on DataMarket
The Time Series Data Library is a collection of about 800 time series that I have maintained since about 1992, and hosted on my personal website. It includes data from a lot of time series textbooks, as well as many other series that I’ve either collected for student projects or helpful people have sent to me. I’ve now moved...
Time series cross-validation 4: forecasting the S&P 500
I finally got around to publishing my time series cross-validation package to github, and I plan to push it out to CRAN shortly. You can clone the repo using github for mac, for windows, or linux, and then run the following script to...
Constants and ARIMA models in R
This post is from my new book Forecasting: principles and practice, available freely online at OTexts.com/fpp/. A non-seasonal ARIMA model can be written as (1) or equivalently as (2) where is the backshift operator, and is the mean of . R uses the parametrization of equation (2). Thus, the inclusion of a constant in a non-stationary ARIMA...
My new forecasting textbook
After years of saying that I was going to write a book to replace Makridakis, Wheelwright and Hyndman (1998), I’m finally ready to make an announcement! My new book is Forecasting: principles and practice, co-authored with George Athanasopoulos. It is available online and free-of-charge. We have written about 2/3 of the book so far (all of which is already...
Measuring time series characteristics
A few years ago, I was working on a project where we measured various characteristics of a time series and used the information to determine what forecasting method to apply or how to cluster the time series into meaningful groups. The two main papers to come out of that project were: Wang, Smith and Hyndman (2006) Characteristic-based clustering for...
Forecasts and ggplot
The forecast package uses the base R graphics for all plots, but some people may prefer to use the nice graphics available using the ggplot2 package. In the following two posts, Frank Davenport shows how it can be done: Plotting forecast() objects in ...
Plotting forecast() objects in ggplot part 1: Extracting the Data
Lately I've been using Rob J Hyndman's excellent forecast package. The package comes with some built in plotting functions but I found I wanted to customize and make my own plots in ggplot. In order to do that, I need a generalizable function that will...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
