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<p>Time series classification (TSC) is a challenging task due to the diversity of types of features 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. 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 <b>other state-of-the-art methods</b> 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 (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10618_2024_1085_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="54" /> </InlineMediaObject> <EquationSource Format="TEX">\({{\,\textrm{ADTW}\,}}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mspace width="0.166667em" /> <mtext>ADTW</mtext> <mspace width="0.166667em" /> </mrow> </math></EquationSource> </InlineEquation>); and (3) cost function tuning. It rationalizes the set of similarity measures employed, reducing the eight base measures of the original PF to <b>four</b> and using the first derivative transform with all similarity measures, rather than a limited subset. <b>It also incorporates HYDRA, a dictionary-based transform.</b> We have re-implemented PF 1.0 and implemented PF 2.0 framework in Java, making the PF framework more efficient.</p>

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Proximity forest 2.0: a new effective and scalable similarity-based classifier for time series

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月14日

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  • Chang Wei Tan,
  • Matthieu Herrmann,
  • Mahsa Salehi,
  • Geoffrey I. Webb

摘要

Time series classification (TSC) is a challenging task due to the diversity of types of features 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. 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 other state-of-the-art 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 ( \({{\,\textrm{ADTW}\,}}\) ADTW ); and (3) cost function tuning. It rationalizes the set of similarity measures employed, reducing the eight base measures of the original PF to four and using the first derivative transform with all similarity measures, rather than a limited subset. It also incorporates HYDRA, a dictionary-based transform. We have re-implemented PF 1.0 and implemented PF 2.0 framework in Java, making the PF framework more efficient.