  Bio-inspired optimization algorithms have been gaining more popularity
recently. One of the most important of these algorithms is particle swarm
optimization (PSO). PSO is based on the collective intelligence of a swam of
particles. Each particle explores a part of the search space looking for the
optimal position and adjusts its position according to two factors; the first
is its own experience and the second is the collective experience of the whole
swarm. PSO has been successfully used to solve many optimization problems. In
this work we use PSO to improve the performance of a well-known representation
method of time series data which is the symbolic aggregate approximation (SAX).
As with other time series representation methods, SAX results in loss of
information when applied to represent time series. In this paper we use PSO to
propose a new minimum distance WMD for SAX to remedy this problem. Unlike the
original minimum distance, the new distance sets different weights to different
segments of the time series according to their information content. This
weighted minimum distance enhances the performance of SAX as we show through
experiments using different time series datasets.
