Anomaly Detection for Monitoring

lies. To fix this problem, the control chart needs to adapt to a changing
mean and spread over time. There are two basic ways to do this:
• Slice up your control chart into smaller time ranges or fixed
windows, and treat each window as its own independent fixed
control chart with a different mean and spread. The values
within each window are used to compute the mean and standard
deviation for that window. Within a small interval, everything
looks like a regular fixed control chart. At a larger scale,
what you have is a control chart that changes across windows.
• Use a moving window, also called a sliding window. Instead of
using predefined time ranges to construct windows, at each
point you generate a moving window that covers the previous N
points. The benefit is that instead of having a fixed mean within
a time range, the mean changes after each value yet still considers
the same number of points to compute the mean.
Moving windows have major disadvantages. You have to keep track
of recent history because you need to consider all of the values that
fall into a window. Depending on the size of your windows, this can
be computationally expensive, especially when tracking a large number
of metrics. Windows also have poor characteristics in the presence
of large spikes. When a spike enters a window, it causes an
abrupt shift in the window until the spike eventually leaves, which
causes another abrupt shift.
Statistical Process Control | 19