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.

翻译：时间序列分类（TSC）是一项具有挑战性的任务，因为不同分类任务相关的特征类型多样，包括趋势、方差、频率、幅值以及各种模式。为应对这一挑战，研究人员开发了多种替代方法，包括基于相似性、特征与区间、形状子序列、词典、核方法、神经网络及混合方法等。尽管核方法、神经网络和混合方法总体表现优异，但某些特定任务更适合采用专用方法。本文提出一种新型基于相似性的分类器——邻近森林2.0版（PF 2.0），其在UCR基准测试中优于此前最先进的基于相似性的分类器，并且在基准测试中更适合基于相似性方法处理的特定数据集上，其性能也优于最先进的核方法、神经网络及混合方法。PF 2.0融合了时间序列相似度量中的三项最新进展：（1）计算高效地提前终止与剪枝以加速弹性相似度计算；（2）一种新型弹性相似度量——修正动态时间规整（ADTW）；（3）代价函数调优。该方法精简了所采用的相似度量集合，将原始PF的八个基础度量减少至三个，并对所有相似度量均使用一阶导数变换（而非仅限于子集）。我们已在单一C++框架中实现了PF 1.0和PF 2.0，从而提升了PF框架的效率。