  The similarity search problem is one of the main problems in time series data
mining. Traditionally, this problem was tackled by sequentially comparing the
given query against all the time series in the database, and returning all the
time series that are within a predetermined threshold of that query. But the
large size and the high dimensionality of time series databases that are in use
nowadays make that scenario inefficient. There are many representation
techniques that aim at reducing the dimensionality of time series so that the
search can be handled faster at a lower-dimensional space level. The symbolic
aggregate approximation (SAX) is one of the most competitive methods in the
literature. In this paper we present a new method that improves the performance
of SAX by adding to it another exclusion condition that increases the exclusion
power. This method is based on using two representations of the time series:
one of SAX and the other is based on an optimal approximation of the time
series. Pre-computed distances are calculated and stored offline to be used
online to exclude a wide range of the search space using two exclusion
conditions. We conduct experiments which show that the new method is faster
than SAX.
