  We present FQN (Fast $Q_n$), a novel algorithm for fast detection of outliers
in data streams. The algorithm works in the sliding window model, checking if
an item is an outlier by cleverly computing the $Q_n$ scale estimator in the
current window. We thoroughly compare our algorithm for online $Q_n$ with the
state of the art competing algorithm by Nunkesser et al, and show that FQN (i)
is faster, (ii) its computational complexity does not depend on the input
distribution and (iii) it requires less space. Extensive experimental results
on synthetic datasets confirm the validity of our approach.
