  Time series classification is an increasing research topic due to the vast
amount of time series data that are being created over a wide variety of
fields. The particularity of the data makes it a challenging task and different
approaches have been taken, including the distance based approach. 1-NN has
been a widely used method within distance based time series classification due
to it simplicity but still good performance. However, its supremacy may be
attributed to being able to use specific distances for time series within the
classification process and not to the classifier itself. With the aim of
exploiting these distances within more complex classifiers, new approaches have
arisen in the past few years that are competitive or which outperform the 1-NN
based approaches. In some cases, these new methods use the distance measure to
transform the series into feature vectors, bridging the gap between time series
and traditional classifiers. In other cases, the distances are employed to
obtain a time series kernel and enable the use of kernel methods for time
series classification. One of the main challenges is that a kernel function
must be positive semi-definite, a matter that is also addressed within this
review. The presented review includes a taxonomy of all those methods that aim
to classify time series using a distance based approach, as well as a
discussion of the strengths and weaknesses of each method.
