Blog Archives

Forecasting within limits

February 21, 2014
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Forecasting within limits

It is common to want forecasts to be positive, or to require them to be within some specified range . Both of these situations are relatively easy to handle using transformations. Positive forecasts To impose a positivity constraint, simply work on the log scale. With the forecast package in R, this can be handled by specifying the Box-Cox parameter...

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Backcasting in R

February 19, 2014
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Backcasting in R

Sometimes it is useful to “backcast” a time series — that is, forecast in reverse time. Although there are no in-built R functions to do this, it is very easy to implement. Suppose x is our time series and we want to backcast for periods. Here is some code that should work for most univariate time series. The example...

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Global energy forecasting competitions

February 19, 2014
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Global energy forecasting competitions

The 2012 GEFcom competition was a great success with several new innovative forecasting methods introduced. These have been published in the IJF as follows: Hong, Pinson and Fan. Global Energy Forecasting Competition 2012 Charleton and Singleton. A refined parametric model for short term load forecasting Lloyd. GEFCom2012 hierarchical load forecasting: Gradient boosting machines and Gaussian processes Nedelec, Cugliari and Goude: GEFCom2012: Electric...

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Hierarchical forecasting with hts v4.0

February 12, 2014
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A new version of my hts package for R is now on CRAN. It was completely re-written from scratch. Not a single line of code survived. There are some minor syntax changes, but the biggest change is speed and scope. This version is many times faster than the previous version and can handle hundreds of thousands of time series...

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Detecting seasonality

February 7, 2014
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Detecting seasonality

I occasionally get email asking how to detect whether seasonality is present in a data set. Sometimes the period of the potential seasonality is known, but in other cases it is not. I’ve discussed before how to estimate an unknown seasonal period, and how to measure the strength of the seasonality. In this post, I want to look at...

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Feedback on OTexts covers please

February 5, 2014
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We are currently selecting the cover design for OTexts books. The first one to go into print will be Forecasting: principles and practice. We have narrowed the choice to the two designs below, although changes are still possible. I thought it would be useful to get some feedback on these designs from readers of this blog (and from people...

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Interview for the Capital of Statistics

February 4, 2014
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Earo Wang recently interviewed me for the Chinese website Capital of Statistics. The English transcript of the intervew is on Earo’s personal website. This is the third interview I’ve done in the last 18 months. The others were for: Data Mining Research. Republished in Amstat News. DecisionStats.  

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Computational Actuarial Science with R

February 2, 2014
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Computational Actuarial Science with R

I recently co-authored a chapter on “Prospective Life Tables” for this book, edited by Arthur Charpentier. R code to reproduce the figures and to complete the exercises for our chapter is now available on github. Code for the other chapters should also be available soon. The book can be pre-ordered on Amazon.

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Automatic time series forecasting in Granada

January 30, 2014
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In two weeks I am presenting a workshop at the University of Granada (Spain) on Automatic Time Series Forecasting. Unlike most of my talks, this is not intended to be primarily about my own research. Rather it is to provide a state-of-the-art overview of the topic (at a level suitable for Masters students in Computer Science). I thought I’d provide...

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Free books on statistical learning

January 29, 2014
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Hastie, Tibshirani and Friedman’s Elements of Statistical Learning first appeared in 2001 and is already a classic. It is my go-to book when I need a quick refresher on a machine learning algorithm. I like it because it is written using the language and perspective of statistics, and provides a very useful entry point into the literature of machine...

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