# Effectiveness of High-Dimensional Distance Metrics on Solar Flare Time Series

arXiv: https://arxiv.org/abs/2511.01873 · HTML: https://mlsanity.com/paper/2511.01873/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 43 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [aeon: a Python toolkit for learning from time series](https://mlsanity.com/paper/2406.14231/) (2024)
- [Fast and Eager k-Medoids Clustering: O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithms](https://mlsanity.com/paper/2008.05171/) (2020)
- [How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events](https://mlsanity.com/paper/2103.07542/) (2021)
- [A Review and Evaluation of Elastic Distance Functions for Time Series Clustering](https://mlsanity.com/paper/2205.15181/) (2022)
- [Challenges with Extreme Class-Imbalance and Temporal Coherence: A Study on Solar Flare Data](https://mlsanity.com/paper/1911.09061/) (2019)
- [Characterizing and comparing external measures for the assessment of cluster analysis and community detection](https://mlsanity.com/paper/2102.00708/) (2021)
- [Improving Solar Flare Prediction by Time Series Outlier Detection](https://mlsanity.com/paper/2206.07197/) (2022)
- [Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 Methods](https://mlsanity.com/paper/2109.14770/) (2021)
- [Multiscale Dubuc: A New Similarity Measure for Time Series](https://mlsanity.com/paper/2411.10418/) (2024)

## Abstract (arXiv)

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.