Recent benchmarking analysis has found that few time series classification algorithms perform better than the Dynamic Time Warping and Rotation Forest benchmark classifiers, whilst the best alternative (COTE) was identified as being hugely computationally expensive \cite{Bagnall_2016}. It's worth noting that feature alignment techniques that calculate relative feature-based distance measures between time series (such as whole series and interval approaches) can be used to calculate single value representations of the similarity between any given pair of time series, including complex time series with multiple dimensions, which can subsequently be used in further clustering or wider classification analysis.
In the case of Dynamic Time Warping feature alignment is achieved by stretching portions of two signals, X and Y, onto a shared set of instances such that a global signal-to-signal distance measure is minimised. The set of distortion paths used in this minimisation problem are based on a lattice of all possible distances between the mth data point of X and the nth data point of Y. Valid warping paths, parameterised by two sequences of the same length, are a combination of “chess king” moves which completely aligns the signal, does not skip any data points, and does not repeat any signal features. In determining the path with minimum warping path the algorithm forces similar features to appear at the same location on a common time axis \cite{Dynamic_time_warping_MATLAB}.