  This article introduces the Event based Prediction Suffix Tree (EPST), a
biologically inspired, event-based prediction algorithm. The EPST learns a
model online based on the statistics of an event based input and can make
predictions over multiple overlapping patterns. The EPST uses a representation
specific to event based data, defined as a portion of the power set of event
subsequences within a short context window. It is explainable, and possesses
many promising properties such as fault tolerance, resistance to event noise,
as well as the capability for one-shot learning. The computational features of
the EPST are examined in a synthetic data prediction task with additive event
noise, event jitter, and dropout. The resulting algorithm outputs predicted
projections for the near term future of the signal, which may be applied to
tasks such as event based anomaly detection or pattern recognition.
