Introduction

Inspired by this Netflix post, I decided to write a post based on this topic using R.

There are several nice packages to achieve this goal, the one we´re going to review is AnomalyDetection.

Download full -and tiny- R code of this post here.

Normal Vs. Abnormal

The definition for abnormal, or outlier, is an element which does not follow the behaviour of the majority.

Data has noise, same example as a radio which doesn't have good signal, and you end up listening to some background noise.

The orange section could be noise in data , since it oscillates around a value without showing a defined pattern, in other words: White noise

, since it oscillates around a value without showing a defined pattern, in other words: White noise Are the red circles noise or they are peaks from an undercover pattern?

A good algorithm can detect abnormal points considering the inner noise and leaving it behind. The AnomalyDetectionTs in AnomalyDetection package can perform this task quite well.

Hands on anomaly detection!

In this example, data comes from the well known wikipedia, which offers an API to download from R the daily page views given any {term + language} .

In this case, we've got page views from term fifa , language en , from 2013-02-22 up to today.

After applying the algorithm, we can plot the original time series plus the abnormal points in which the page views were over the expected value.

About the algorithm

Parameters in algorithm are max_anoms=0.01 (to have a maximum of 0.01% outliers points in final result), and direction="pos" to detect anomalies over (not below) the expected value.

As a result, 8 anomalies dates were detected. Additionally, the algorithm returns what it would have been the expected value, and an extra calculation is performed to get this value in terms of percentage perc_diff .

If you want to know more about the maths behind it, google: Generalized ESD and time series decomposition

Something went wrong:

Something strange since 1st expected value is the same value as the series has ( 34028 page views). As a matter of fact perc_diff is 0 while it should be a really low number. However the anomaly is well detected and apparently next ones too. If you know why, you can email and share the knowledge :)

Discovering anomalies

Last plot shows a line indicating linear trend over an specific period -clearly decreasing-, and two black circles. It's interesting to note that these black points were not detected by the algorithm because they are part of a decreasing tendency (noise perhaps?).

A really nice shot by this algorithm since the focus on detections are on the changes of general patterns. Just take a look at the last detected point in that period, it was a peak that didn't follow the decreasing pattern (occurred on 2014-07-12 ).

Checking with the news

These anomalies with the term fifa are correlated with the news, the first group of anomalies is related with the FIFA World Cup (around Jun/Jul 2014), and the second group centered on May 2015 is related with FIFA scandal.

In the LA Times it can be found a timeline about the scandal, and two important dates -May 27th and 28th-, which are two dates found by the algorithm.

Next step

There is a complete chapter in the Data Science Live Book which covers the outliers treatment issue, which can be seen in a way as some kind of anomalous data. All the examples are in R and the topic is covered from both perspectives, practical and theoretical.

📖 Outliers treatment chapter.

Data Science Live Book (open source)

📌 Continue learning about machine learning data science with the Data Science Live Book (https://livebook.datascienceheroes.com). Fully available on-line!

Book's download page 📥📘

Thanks for reading :)