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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.01873 |
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| _version_ | 1866912686539800576 |
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| author | Rohlfing, Elaina Ahmadzadeh, Azim Aparna, V |
| author_facet | Rohlfing, Elaina Ahmadzadeh, Azim Aparna, V |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01873 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Effectiveness of High-Dimensional Distance Metrics on Solar Flare Time Series Rohlfing, Elaina Ahmadzadeh, Azim Aparna, V Solar and Stellar Astrophysics Machine Learning 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. |
| title | Effectiveness of High-Dimensional Distance Metrics on Solar Flare Time Series |
| topic | Solar and Stellar Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2511.01873 |