Beginning Anomaly Detection Using

 Anomalies in a Time Series

With the introduction of time as a variable, you are now dealing with a notion of 

temporality associated with the data sets. What this means is that certain patterns 

can emerge based on the time stamp, so you can see monthly occurrences of some 

phenomenon.

To better understand time-series based anomalies, let’s take a random person and 

look into his/her spending habits over some arbitrary month (Figure 

1-8

).

Assume the initial spike in expenditures at the start of the month is due to the 

payment of bills like rent and insurance. During the weekdays, our person occasionally 

eats out, and on the weekends goes shopping for groceries, clothes, or just various items.

These expenditures can vary from month to month from the influence of various 

holidays. Let’s take a look at November, when you can expect a massive spike in 

purchases on Black Friday (Figure 

1-9

).