Machine Learning in Java - Second Edition

Histogram-based anomaly detection

In histogram-based anomaly detection, we split the signals by a selected time window, as shown in the following diagram.

For each window, we calculate the histogram; that is, for a selected number of buckets, we count how many values fall into each bucket. The histogram captures the basic distribution of values in a selected time window, as shown in the center of the diagram.

Histograms can then be directly presented as instances, where each bin corresponds to an attribute. Furthermore, we can reduce the number of attributes by applying a dimensionality-reduction technique, such as Principal Component Analysis (PCA), which allows us to visualize the reduced-dimension histograms in a plot, as shown at the ...