There are some tools that I use regularly, and I would like my research students and post-docs to learn them too. Here are some great online tutorials that might help.
Last week I gave a talk in the Yahoo! Big Thinkers series. The video of the talk is now online and embedded below.
For the next few weeks I am travelling in North America and will be giving the following talks.
- 19 June: Southern California Edison, Rosemead CA.
“Probabilistic forecasting of peak electricity demand”.
- 23 June: International Symposium on Forecasting, Riverside CA.
“MEFM: An R package for long-term probabilistic forecasting of electricity demand”.
- 25 June: Google, Mountain View, CA.
“Automatic algorithms for time series forecasting”.
- 26 June: Yahoo, Sunnyvale, CA.
“Exploring the boundaries of predictability: what can we forecast, and when should we give up?”
- 30 June: Workshop on Frontiers in Functional Data Analysis, Banff, Canada.
“Exploring the feature space of large collections of time series”.
The Yahoo talk will be streamed live.
I’ll post slides on my main site after each talk.
Every now and then a commercial software vendor makes claims on social media about how their software is so much better than the forecast package for R, but no details are provided.
There are lots of reasons why you might select a particular software solution, and R isn’t for everyone. But anyone claiming superiority should at least provide some evidence rather than make unsubstantiated claims. Continue reading →
The anomalous package provides some tools to detect unusual time series in a large collection of time series. This is joint work with Earo Wang (an honours student at Monash) and Nikolay Laptev (from Yahoo Labs). Yahoo is interested in detecting unusual patterns in server metrics. Continue reading →
This week I uploaded a new version of the forecast package to CRAN. As there were a lot of changes, I decided to increase the version number to 6.0.
Yahoo Labs has just released an interesting new data set useful for research on detecting anomalies (or outliers) in time series data. There are many contexts in which anomaly detection is important. For Yahoo, the main use case is in detecting unusual traffic on Yahoo servers. Continue reading →
I spend much of my day sitting in front of a screen, coding or writing. To limit the strain on my eyes, I use a dark theme as much as possible. That is, I write with light colored text on a dark background. I don’t know why this is not the default in more software as it makes a big difference after a few hours of writing.
Most of the time, I am writing using either Sublime Text, RStudio or TeXstudio. Each of them can be set to use a dark theme with syntax coloring to highlight structural features in the text.
Continue reading →