As mentioned in my previous post on the forecast package v7, the most visible feature was the introduction of ggplot2 graphics. This post briefly summarizes the remaining new features of forecast v7.
Continue reading →
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.
In just over three weeks, the inaugural MeDaScIn event will take place. This is an initiative to grow the talent pool of local data scientists and to promote Melbourne as a world city of excellence in Data Science.
The main event takes place on Friday 6th May, with lots of interesting sounding titles and speakers from business and government. I’m the only academic speaker on the program, giving the closing talk on “Automatic FoRecasting”. Earlier in the day I am running a forecasting workshop where I will discuss forecasting issues and answer questions for about 90 minutes. There are still a few places left for the main event, and for the workshops. Book soon if you want to attend.
Almost exactly 20 years ago I wrote a paper with Yanan Fan on how sample quantiles are computed in statistical software. It was cited 43 times in the first 10 years, and 457 times in the next 10 years, making it my third paper to receive 500+ citations.
So what happened in 2006 to suddenly increase the citations? I think it was a combination of things: Continue reading →
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 spoke to our new crop of honours students this morning. Here are my slides, example files and links. Continue reading →
The first rOpenSci unconference in Australia will be held on Thursday and Friday (April 21-22) in Brisbane, at the Microsoft Innovation Centre.
This event will bring together researchers, developers, data scientists and open data enthusiasts from industry, government and university. The aim is to conceptualise and develop R-based tools that address current challenges in data science, open science and reproducibility.
You can view more details, see who else is attending, and most importantly, apply to attend at the website.
Our research group been growing lately, as you can see below! We were featured in the latest issue of the Monash newsletter The Insider. Check it out.
From today’s email:
I wanted to ask you about your R forecast package, in particular the Arima() function. We are using this function to fit an ARIMAX model and produce model estimates and standard errors, which in turn can be used to get p-values and later model forecasts. To double check our work, we are also fitting the same model in SAS using PROC ARIMA and comparing model coefficients and output. Continue reading →