  Time series classification (TSC) is a challenging task due to the diversity
of types of feature that may be relevant for different classification tasks,
including trends, variance, frequency, magnitude, and various patterns. To
address this challenge, several alternative classes of approach have been
developed, including similarity-based, features and intervals, shapelets,
dictionary, kernel, neural network, and hybrid approaches. While kernel, neural
network, and hybrid approaches perform well overall, some specialized
approaches are better suited for specific tasks. In this paper, we propose a
new similarity-based classifier, Proximity Forest version 2.0 (PF 2.0), which
outperforms previous state-of-the-art similarity-based classifiers across the
UCR benchmark and outperforms state-of-the-art kernel, neural network, and
hybrid methods on specific datasets in the benchmark that are best addressed by
similarity-base methods. PF 2.0 incorporates three recent advances in time
series similarity measures -- (1) computationally efficient early abandoning
and pruning to speedup elastic similarity computations; (2) a new elastic
similarity measure, Amerced Dynamic Time Warping (ADTW); and (3) cost function
tuning. It rationalizes the set of similarity measures employed, reducing the
eight base measures of the original PF to three and using the first derivative
transform with all similarity measures, rather than a limited subset. We have
implemented both PF 1.0 and PF 2.0 in a single C++ framework, making the PF
framework more efficient.
