I’m currently in the Netherlands for a few weeks, and I’ll be giving a seminar at the Data Science Centre in Eindhoven next Wednesday afternoon on “Visualization of big time series data”. Details follow. Continue reading →
I’ve pushed a minor update to the forecast package to CRAN. Some highlights are listed here.
Version 7 of the forecast package was released on CRAN about a month ago, but I'm only just getting around to posting about the new features.
The most visible feature was the introduction of ggplot2 graphics. I first wrote the forecast package before ggplot2 existed, and so only base graphics were available. But I figured it was time to modernize and use the nice features available from ggplot2. The following examples illustrate the main new graphical functionality.
For illustration purposes, I'm using the male and female monthly deaths from lung diseases in the UK.
I often see figures with two sets of prediction intervals plotted on the same graph using different line types to distinguish them. The results are almost always unreadable. A better way to do this is to use semi-transparent shaded regions. Here is an example showing two sets of forecasts for the Nile River flow.
library(forecast) f1 = forecast(auto.arima(Nile, lambda=0), h=20, level=95) f2 = forecast(ets(Nile), h=20, level=95) plot(f1, shadecol=rgb(0,0,1,.4), flwd=1, main="Forecasts of Nile River flow", xlab="Year", ylab="Billions of cubic metres") polygon(c(time(f2$mean),rev(time(f2$mean))), c(f2$lower,rev(f2$upper)), col=rgb(1,0,0,.4), border=FALSE) lines(f2$mean, col=rgb(.7,0,0)) legend("bottomleft", fill=c(rgb(0,0,1,.4),rgb(1,0,0,.4)), col=c("blue","red"), lty=1, legend=c("ARIMA","ETS"))
The blue region shows 95% prediction intervals for the ARIMA forecasts, while the red region shows 95% prediction intervals for the ETS forecasts. Where they overlap, the colors blend to make purple. In this case, the point forecasts are quite close, but the prediction intervals are relatively different.
I’ve always struggled with using
plotmath via the
expression function in R for adding mathematical notation to axes or legends. For some reason, the most obvious way to write something never seems to work for me and I end up using trial and error in a loop with far too many iterations.
At the recent International Symposium on Forecasting, held in Riverside, California, Tillman Gneiting gave a great talk on “Evaluating forecasts: why proper scoring rules and consistent scoring functions matter”. It will be the subject of an IJF invited paper in due course.
Big data is now endemic in business, industry, government, environmental management, medical science, social research and so on. One of the commensurate challenges is how to effectively model and analyse these data.
This workshop will bring together national and international experts in statistical modelling and analysis of big data, to share their experiences, approaches and opinions about future directions in this field.
I’m currently visiting Taiwan and I’m giving two seminars while I’m here — one at the National Tsing Hua University in Hsinchu, and the other at Academia Sinica in Taipei. Details are below for those who might be nearby. Continue reading →