  Increasingly large data series collections are becoming commonplace across
many different domains and applications. A key operation in the analysis of
data series collections is similarity search, which has attracted lots of
attention and effort over the past two decades. Even though several relevant
approaches have been proposed in the literature, none of the existing studies
provides a detailed evaluation against the available alternatives. The lack of
comparative results is further exacerbated by the non-standard use of
terminology, which has led to confusion and misconceptions. In this paper, we
provide definitions for the different flavors of similarity search that have
been studied in the past, and present the first systematic experimental
evaluation of the efficiency of data series similarity search techniques. Based
on the experimental results, we describe the strengths and weaknesses of each
approach and give recommendations for the best approach to use under typical
use cases. Finally, by identifying the shortcomings of each method, our
findings lay the ground for solid further developments in the field.
