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用于空间天气数据分析的多元时间序列数据集。

Multivariate time series dataset for space weather data analytics.

机构信息

Department of Computer Science, Georgia State University, Atlanta, United States.

Department of Physics & Astronomy, Georgia State University, Atlanta, United States.

出版信息

Sci Data. 2020 Jul 10;7(1):227. doi: 10.1038/s41597-020-0548-x.

DOI:10.1038/s41597-020-0548-x
PMID:32651380
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7351763/
Abstract

We introduce and make openly accessible a comprehensive, multivariate time series (MVTS) dataset extracted from solar photospheric vector magnetograms in Spaceweather HMI Active Region Patch (SHARP) series. Our dataset also includes a cross-checked NOAA solar flare catalog that immediately facilitates solar flare prediction efforts. We discuss methods used for data collection, cleaning and pre-processing of the solar active region and flare data, and we further describe a novel data integration and sampling methodology. Our dataset covers 4,098 MVTS data collections from active regions occurring between May 2010 and December 2018, includes 51 flare-predictive parameters, and integrates over 10,000 flare reports. Potential directions toward expansion of the time series, either "horizontally" - by adding more prediction-specific parameters, or "vertically" - by generalizing flare into integrated solar eruption prediction, are also explained. The immediate tasks enabled by the disseminated dataset include: optimization of solar flare prediction and detailed investigation for elusive flare predictors or precursors, with both operational (research-to-operations), and basic research (operations-to-research) benefits potentially following in the future.

摘要

我们介绍并公开提供了一个综合的多变量时间序列(MVTS)数据集,该数据集从 Spaceweather HMI Active Region Patch(SHARP)系列中的太阳色球矢量磁图中提取。我们的数据集还包括一个经过交叉检查的 NOAA 太阳耀斑目录,这立即为太阳耀斑预测工作提供了便利。我们讨论了用于收集、清理和预处理太阳活动区和耀斑数据的方法,并进一步描述了一种新颖的数据集成和采样方法。我们的数据集涵盖了 2010 年 5 月至 2018 年 12 月期间发生的 4098 个 MVTS 数据集,包括 51 个耀斑预测参数,并集成了超过 10000 次耀斑报告。还解释了扩展时间序列的潜在方向,无论是“横向”——通过添加更多预测特定的参数,还是“纵向”——通过将耀斑概括为综合太阳爆发预测。传播数据集所启用的直接任务包括:优化太阳耀斑预测,并对难以捉摸的耀斑预测因子或前兆进行详细研究,这可能带来操作(从研究到操作)和基础研究(从操作到研究)的好处。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22a1/7351763/bdd85026c937/41597_2020_548_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22a1/7351763/dd38c00ad7a6/41597_2020_548_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22a1/7351763/1df0eabb4be8/41597_2020_548_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22a1/7351763/c6d13b90df89/41597_2020_548_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22a1/7351763/4771af5ee915/41597_2020_548_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22a1/7351763/84ff6b3ee607/41597_2020_548_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22a1/7351763/bdd85026c937/41597_2020_548_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22a1/7351763/dd38c00ad7a6/41597_2020_548_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22a1/7351763/1df0eabb4be8/41597_2020_548_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22a1/7351763/c6d13b90df89/41597_2020_548_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22a1/7351763/4771af5ee915/41597_2020_548_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22a1/7351763/84ff6b3ee607/41597_2020_548_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22a1/7351763/bdd85026c937/41597_2020_548_Fig6_HTML.jpg

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