提出新评估方法与基准，让时间序列模式发现效果可量化对比
Quantitative Evaluation of Motif Sets in Time Series
- 设计通用评估指标PROM，突破旧方法隐含假设限制
- 构建更难的基准TSMD-Bench，推动方法公平比较
- 支持大规模系统性对比，适合算法研究者使用
时间序列模式发现（TSMD）旨在识别时间序列中的重复模式，在众多应用领域中至关重要，已有多种方法。但现有方法多依赖定性评估，少数定量指标常隐含特定假设，适用范围受限。本文提出PROM——一种普适性强的评估指标，以及TSMD-Bench——一个更具有挑战性的基准数据集。实验表明，PROM能提供比现有指标更全面的评估，TSMD-Bench比早期基准更具挑战性，两者结合可有效揭示不同TSMD方法的相对性能。总体而言，该方法为该领域的大规模、系统性性能比较提供了可能。
原文摘要 · Abstract (English)
Time Series Motif Discovery (TSMD), which aims at finding recurring patterns in time series, is an important task in numerous application domains, and many methods for this task exist. These methods are usually evaluated qualitatively. A few metrics for quantitative evaluation, where discovered motifs are compared to some ground truth, have been proposed, but they typically make implicit assumptions that limit their applicability. This paper introduces PROM, a broadly applicable metric that overcomes those limitations, and TSMD-Bench, a benchmark for quantitative evaluation of time series motif discovery. Experiments with PROM and TSMD-Bench show that PROM provides a more comprehensive evaluation than existing metrics, that TSMD-Bench is a more challenging benchmark than earlier ones, and that the combination can help understand the relative performance of TSMD methods. More generally, the proposed approach enables large-scale, systematic performance comparisons in this field.
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