HiSAXy: A fast methodology for solar wind structure identification in millions of time series

TL;DR

This paper introduces HiSAXy, a hybrid unsupervised clustering method combining iSAX and HDBSCAN, to efficiently identify magnetic structures in solar wind time series, reducing manual analysis while maintaining high accuracy.

Contribution

The paper presents a novel hybrid clustering algorithm, HiSAXy, for rapid and automated identification of magnetic structures in large-scale solar wind data.

Findings

Successfully identified small-scale IMF discontinuities

Significantly reduced human analysis hours

Maintained high self-similarity within clusters

Abstract

We present a hybridized unsupervised clustering algorithm Hisaxy as a novel way to identify frequently occurring magnetic structures embedded in the interplanetary magnetic field (IMF) carried by the solar wind. The Hisaxy algorithm utilizes a combination of indexable Symbolic Aggregate approXimation (iSAX) and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to efficiently identify clusters of patterns embedded in time series data. We utilized Hisaxy to identify small-scale structures, known as discontinuities, embedded in time series measurements of the IMF. In doing so, we demonstrate the capability of the algorithm to significantly reduce the amount of human analysis hours required to identify these structures, all the while maintaining a high degree of self similarity within a given cluster of time series data.