Anomaly Detection for Monitoring

ack again, meaning that they smooth data using both past and
future data. Smoothing bidirectionally can eliminate the effects of
large spikes.
Figure 3-5. A window function control chart. This time, the window is
formed with values on both sides of the current value. As a result,
anomalous spikes won’t generate abrupt shifts in control limits even
when they first enter the window.
The downside to window functions is that they require a larger time
delay, which is a result of not knowing the smoothed value until
enough future values have been observed. This is because when you
center a bidirectional windowing function on “now,” it extends into
the future. In practice, EWMAs are a good enough compromise for
situations where you can’t measure or wait for future values.
Control charts based on bidirectional smoothing have the following
characteristics:
• They will introduce time lag into calculations. If you smooth
symmetrically over 60 second-windows, you won’t know the
smoothed value of “now” until 30 seconds—half the window—
has passed.
• Like sliding windows, they require more memory and CPU to
compute.
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