高维太阳耀斑时间序列中，弹性距离不如欧氏距离有效。
Effectiveness of High-Dimensional Distance Metrics on Solar Flare Time Series
- 用k-medoids聚类对比多种高维距离度量
- 所有弹性距离均未显著优于欧氏距离
- 适合研究太阳活动时间序列建模的读者
太阳耀斑预测虽被广泛研究，但仍属开放问题。本文研究弹性距离度量在太阳耀斑数据集SWAN-SF中的模式检测能力。采用简单的k-medoids聚类算法评估先进高维距离度量的有效性。结果表明，尽管经过充分优化，所有弹性距离均未显著优于欧氏距离。我们证明，尽管弹性度量在单变量时间序列中表现良好，但在具有高随机性的多变量太阳活动时间序列SWAN-SF中，其效果实质上退化为欧氏距离。通过数千次实验，提供了定量与定性证据支持该发现。
原文摘要 · Abstract (English)
Solar-flare forecasting has been extensively researched yet remains an open problem. In this paper, we investigate the contributions of elastic distance measures for detecting patterns in the solar-flare dataset, SWAN-SF. We employ a simple $k$-medoids clustering algorithm to evaluate the effectiveness of advanced, high-dimensional distance metrics. Our results show that, despite thorough optimization, none of the elastic distances outperform Euclidean distance by a significant margin. We demonstrate that, although elastic measures have shown promise for univariate time series, when applied to the multivariate time series of SWAN-SF, characterized by the high stochasticity of solar activity, they effectively collapse to Euclidean distance. We conduct thousands of experiments and present both quantitative and qualitative evidence supporting this finding.
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