Experimental Comparison of Representation Methods and Distance Measures
  for Time Series Data
  The previous decade has brought a remarkable increase of the interest in
applications that deal with querying and mining of time series data. Many of
the research efforts in this context have focused on introducing new
representation methods for dimensionality reduction or novel similarity
measures for the underlying data. In the vast majority of cases, each
individual work introducing a particular method has made specific claims and,
aside from the occasional theoretical justifications, provided quantitative
experimental observations. However, for the most part, the comparative aspects
of these experiments were too narrowly focused on demonstrating the benefits of
the proposed methods over some of the previously introduced ones. In order to
provide a comprehensive validation, we conducted an extensive experimental
study re-implementing eight different time series representations and nine
similarity measures and their variants, and testing their effectiveness on
thirty-eight time series data sets from a wide variety of application domains.
In this paper, we give an overview of these different techniques and present
our comparative experimental findings regarding their effectiveness. In
addition to providing a unified validation of some of the existing
achievements, our experiments also indicate that, in some cases, certain claims
in the literature may be unduly optimistic.
