Forecasting competitions are a great way to test new methods and obtain a realistic evaluation of how good they are. So I’m delighted that the IEEE is organizing an energy forecasting competition as outlined by Tao Hong below.
Posts Tagged ‘forecasting’:
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 time series data. Data Mining and Knowledge Discovery, 13(3), 335–364. Wang, Smith-Miles and Hyndman (2009) “Rule induction for forecasting method selection: meta-learning the characteristics of univariate time series”, Neurocomputing, 72, 2581–2594. I’ve since had a lot of requests for the code which one of my coauthors has been helpfully emailing to anyone who asked. But to make it easier, we thought it might be helpful if I post some updated code here. This is not the same as the R code we used in the paper, as I’ve improved it in several ways (so it will give different results). If you just want the code, skip to the bottom of the post.
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 ggplot part 1: Extracting the Data Plotting forecast() objects in ggplot part 2: Visualize Observations, Fits, and Forecasts
Exponential smoothing and regressors
I have thought quite a lot about including regressors (i.e. covariates) in exponential smoothing (ETS) models, and I have done it a couple of times in my published work. See my 2008 exponential smoothing book (chapter 9) and my 2008 Tourism Management paper. However, there are some theoretical issues with these approaches, which have come to light through the research of Ahmad Farid Osman, one of our PhD students at Monash University. Basically, they are never forecastable in the sense explained in Section 10.2 my 2008 book (forecastability is the ETS equivalent of invertibility in ARIMA models). Osman has attempted to repair the problem by proposing a different formulation from those in the above references. The only public description of his proposed model is given by Osman and King in this presentation – sorry, they do have a full paper explaining their approach, but it is not publicly available. However, the model is much messier than the formulation we put in our book, and although it avoids the forecastability issues, I think it is more difficult to interpret. Still, it’s a good attempt at a tough problem, and there’s nothing else around that’s any better. So don’t expect any code for fitting ETS models with regressors to appear in the forecast package
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Are we getting better at forecasting?
I was interviewed recently for the Boston Globe. The interview was by email and I thought it might be useful to post here.
Forecasting time series using R
I gave this talk on Forecasting time series using R for the Melbourne Users of R Network (MelbURN) on Thursday 27 October 2011. Slides Examples Abstract I look at the various facilities for time series forecasting available in R, concentrating on the forecast package. This package implements several automatic methods for forecasting time series including forecasts from ARIMA models, ARFIMA models and exponential smoothing models. I also look more generally at how to go about forecasting non-seasonal data, seasonal data, seasonal data with high frequency, and seasonal data with multiple frequencies. Examples are taken from my own consulting experience. I give an overview of what’s possible and available and where it is useful, rather than give the mathematical details of any specific time series methods.
Cyclic and seasonal time series
These terms get confused all the time (e.g., this question on CrossValidated.com), and so I thought it might be helpful to try to summarize the distinction and some of the associated models.
Kaggle on TV
It is good to see forecasting algorithms getting some mainstream exposure on ABC Catalyst. Update: See also this great talk by Jeremy Howard, a data scientist from Melbourne and now part of Kaggle.
Help for forecasting practitioners
I often get email from forecasters wanting assistance. As much as I’d like to provide a free forecasting advice service to the world, that’s not what I’m paid to do, and I choose to spend my unpaid time on other things. However, there are some very helpful resources available for forecasting practitioners.
Time series cross-validation: an R example
I was recently asked how to implement time series cross-validation in R. Time series people would normally call this “forecast evaluation with a rolling origin” or something similar, but it is the natural and obvious analogue to leave-one-out cross-validation for cross-sectional data, so I prefer to call it “time series cross-validation”.
Rob J Hyndman