2017 International Symposium on Energy Analytics

Predictive Energy Analytics in the Big Data World

Cairns, Australia, June 22-23, 2017

ISEA2017

snorkellers-on-great-barrier-reef

This will be a great conference, and it is in a great location — Cairns, Australia, right by the Great Barrier Reef. Even better, if you stay on you can attend the International Symposium on Forecasting which immediately follows the International Symposium on Energy Analytics.

So block out 22-28 June 2017 on your calendars so you can enjoy a tropical paradise in one of the most beautiful parts of Australia, while attending two awesome conferences.

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forecast v7 and ggplot2 graphics

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.

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Melbourne Data Science Initiative 2016

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.

All the details are here.

Plotting overlapping prediction intervals

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"))

NileRiverFlow

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.

rOpenSci unconference in Brisbane, 21-22 April 2016

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.

Past examples of the projects can herehere, and here. Also here.

You can view more details, see who else is attending, and most importantly, apply to attend at the website.