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.
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 →
From today’s email:
I have just finished reading a copy of ‘Forecasting:Principles and Practice’ and I have found the book really interesting. I have particularly enjoyed the case studies and focus on practical applications.
After finishing the book I have joined a forecasting competition to put what I’ve learnt to the test. I do have a couple of queries about the forecasting outputs required. The output required is a quantile forecast, is this the same as prediction intervals? Is there any R function to produce quantiles from 0 to 99?
If you were able to point me in the right direction regarding the above it would be greatly appreciated.
I occasionally get emails from people thinking they have found a bug in one of my R packages, and I usually have to reply asking them to provide a minimal reproducible example (MRE). This post is to provide instructions on how to create a MRE. Continue reading →
When modelling data with ARIMA models, it is sometimes useful to plot the inverse characteristic roots. The following functions will compute and plot the inverse roots for any fitted ARIMA model (including seasonal models). Continue reading →