Category Archives: math

Monitoring key performance indicators (KPIs), sales or any other product data means working within an ecosystem where very often you will see metrics correlating with each other. When a normal correlation between two metrics is broken, we have reason to suspect something strange is happening.

As an example, take a look at anomaly.io analytics during its early days (a long time ago). In the graphic above, the new users are shown in green and the returning users in red. Clearly, something strange happens in the middle of November. Let’s use some techniques to find out more!
Read More

To determine the level of correlation between various metrics we often use the normalized cross-correlation formula.2
Read More

Previously, we looked at using Twitter Breakout (EDM) to detect Anomalies. As with the popular E-Divisive, EDM detects mean shift and changes in distribution. Both algorithms work with seasonal time series, but perform even better without seasonality. Read More

We previously tested Twitter Anomaly Detection package using the R language. Now let’s take a look at Twitter Breakout Detection.

Read More

Our brains are really fast at recognizing patterns and forms: we can often find the seasonality of a signal in under a second. It is also possible do this with mathematics using the Fourier transform.

First, we will explain what a Fourier transform is. Next, we will find the seasonality of a website from its Google Analytics pageview report using the R language.

Read More

Monitored metrics very often exhibit regular patterns. When the values are correlated with the time of the day, it’s easier to spot anomalies, but it’s harder when they do not. In some cases, it is possible to use machine learning to differentiate the usual patterns from the unusual ones.

Read More