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An Introduction to Anomaly Detection in R with Exploratory.
One of the latest and exciting additions to Exploratory is Anomaly Detection support, which is literally to detect anomalies in the time series data. So what is ‘ anomaly ’ anyway?
Let’s say you are looking at your website page views, there is a trend that goes up and down.
But then, you might see big jumps or drops that are unusual time to time, like the ones with the red circle below.
It could be that these were the days when you started new marketing campaigns or your products or services were featured by popular media posts like TechCrunch, for example. These are what we call ‘anomalies’.
The tricky thing here is that this type of trend data usually goes up and down every day, week, or any time period. For example, let’s say we had a huge page visit at the end of the last month. But if we have such jumps at every end of the month, then that’s just a monthly trend and it’s actually a normal thing.
Or, if you have a consumer retail business, most likely your website would expect larger traffic around the holiday season, which you would expect every year and wouldn’t consider it as anomaly unless it is much larger than the other holiday seasons in the past.
So you want to take the general pattern of the underlying trend as well as ‘seasonality’ into account before making the judgment of whether the larger than usual traffic is truly anomaly or not.
Anomaly Detection in R.
As you would guess, there are many anomaly detection algorithms prov >(By the way, there are more than 10,000 ‘official’ packages just on CRAN repository alone today, not counting the ones on Github or other repositories.)
But this package called ‘AnomalyDetection’, which was developed by Arun Kejariwal and others at Twitter, is so far the best in terms of the quality and the ease of use. It employs an algorithm referred to as Seasonal Hybrid ESD (S-H-ESD), which can detect both global as well as local anomalies in the time series data by taking seasonality and trend into account. The team at Twitter needed something robust and practical to monitor their traffics and detect anomalies so they built this in R.
And as always, we wanted to make it even easier to access it in Exploratory — UI for R. Here is how.
Anomaly Detection in Exploratory.
Prepare Data.
For this demonstration, we’re going to use this sample Google Analytics unique page view data for a fictional company. You can import download the EDF and import it into Exploratory from ‘File Data’ menu.

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