Blog Archives

More time series data online

February 27, 2014
By

Earlier this week I had coffee with Ben Fulcher who told me about his online collection comprising about 30,000 time series, mostly medical series such as ECG measurements, meteorological series, birdsong, etc. There are some finance series, but not ma...

Read more »

The forecast mean after back-transformation

February 24, 2014
By
The forecast mean after back-transformation

Many functions in the forecast package for R will allow a Box-Cox transformation. The models are fitted to the transformed data and the forecasts and prediction intervals are back-transformed. This preserves the coverage of the prediction intervals, and the back-transformed point forecast can be considered the median of the forecast densities (assuming the forecast densities on the transformed scale...

Read more »

Forecasting within limits

February 21, 2014
By
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...

Read more »

Backcasting in R

February 19, 2014
By
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...

Read more »

Global energy forecasting competitions

February 19, 2014
By
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...

Read more »

Hierarchical forecasting with hts v4.0

February 12, 2014
By

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...

Read more »

Detecting seasonality

February 7, 2014
By
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...

Read more »

Feedback on OTexts covers please

February 5, 2014
By

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...

Read more »

Interview for the Capital of Statistics

February 4, 2014
By

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.  

Read more »

Computational Actuarial Science with R

February 2, 2014
By
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.

Read more »