The Time Window Distribution model focuses on the timestamp and the occurrence of the events.
ADWINÂ
The idea of ADWIN is to start from time window W and dynamically grow the window W when there is no apparent change in the context, and shrink it when a change is detected. The algorithm tries to find two subwindows of W – w_{0} and w_{1} that exhibit distinct averages. This means that the older portion of the window – w_{0} is based on a data distribution different than the actual one, and is therefore dropped.
